diff --git a/supervised-airfoils.ipynb b/supervised-airfoils.ipynb index f0024c2..f817df9 100644 --- a/supervised-airfoils.ipynb +++ b/supervised-airfoils.ipynb @@ -1,790 +1,811 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "qT_RWmTEugu9" - }, - "source": [ - "# Supervised training for RANS flows around airfoils\n", - "\n", - "## Overview \n", - "\n", - "For this example of supervised training\n", - "we have a turbulent airflow around wing profiles, and we'd like to know the average motion\n", - "and pressure distribution around this airfoil for different Reynolds numbers and angles of attack.\n", - "Thus, given an airfoil shape, Reynolds numbers, and angle of attack, we'd like to obtain\n", - "a velocity field and a pressure field around the airfoil.\n", - "\n", - "This is classically approximated with _Reynolds-Averaged Navier Stokes_ (RANS) models, and this\n", - "setting is still one of the most widely used applications of Navier-Stokes solver in industry.\n", - "However, instead of relying on traditional numerical methods to solve the RANS equations,\n", - "we now aim for training a surrogate model via a neural network that completely bypasses the numerical solver,\n", - "and produces the solution in terms of velocity and pressure.\n", - "[[run in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/supervised-airfoils.ipynb)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2ZaC0_MdJMTH" - }, - "source": [ - "## Formulation\n", - "\n", - "\n", - "With the supervised formulation from {doc}`supervised`, our learning task is pretty straight-forward, and can be written as \n", - "\n", - "$$\\begin{aligned}\n", - "\\text{arg min}_{\\theta} \\sum_i ( f(x_i ; \\theta)-y^*_i )^2 ,\n", - "\\end{aligned}$$\n", - "\n", - "where $x$ and $y^*$ each consist of a set of physical fields,\n", - "and the index $i$ evaluates the difference across all discretization points in our data sets.\n", - "\n", - "The goal is to infer velocity $\\mathbf{u} = u_x,u_y$ and a pressure field $p$ in a computational domain $\\Omega$\n", - "around the airfoil in the center of $\\Omega$. \n", - "$u_x,u_y$ and $p$ each have a dimension of $128^2$.\n", - "As inputs we have the Reynolds number $\\text{Re} \\in \\mathbb{R}$, the angle of attack\n", - "$\\alpha \\in \\mathbb{R}$, and the airfoil shape $\\mathbf{s}$ encoded as a rasterized grid with $128^2$.\n", - "Both constant, scalar inputs $\\text{Re}$ and $\\alpha$ are likewise extended to a size of $128^2$.\n", - "Thus, put together, both input and output have the same dimensions: $x,y^* \\in \\mathbb{R}^{3\\times128\\times128}$.\n", - "This is exactly what we'll specify as input and output dimensions for the NN below.\n", - "\n", - "A point to keep in mind here is that our quantities of interest in $y^*$ contain three different physical fields. While the two velocity components are quite similar in spirit, the pressure field typically has a different behavior with an approximately squared scaling with respect to the velocity (cf. [Bernoulli](https://en.wikipedia.org/wiki/Bernoulli%27s_principle)). This implies that we need to be careful with simple summations (as in the minimization problem above), and that we should take care to normalize the data.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ewKoLbFCJMTJ" - }, - "source": [ - "## Code coming up...\n", - "\n", - "Let's get started with the implementation. Note that we'll skip the data generation process here. The code below is adapted from {cite}`thuerey2020dfp` and [this codebase](https://github.com/thunil/Deep-Flow-Prediction), which you can check out for details. Here, we'll simply download a small set of training data generated with a Spalart-Almaras RANS simulation in [OpenFOAM](https://openfoam.org/)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JwZudtWauiGa", - "outputId": "802dc68e-ac0d-4a0e-ffb0-30153433b98b" - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Torch version 1.7.1\n", - "Loaded data, 320 training, 80 validation samples\n", - "Size of the inputs array: (320, 3, 128, 128)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import os.path, random\n", - "import torch\n", - "from torch.utils.data import Dataset\n", - "print(\"Torch version {}\".format(torch.__version__))\n", - "\n", - "# get training data\n", - "dir = \"./\"\n", - "if True:\n", - " # download\n", - " if not os.path.isfile('data-airfoils.npz'):\n", - " import requests\n", - " print(\"Downloading training data (300MB), this can take a few minutes the first time...\")\n", - " with open(\"data-airfoils.npz\", 'wb') as datafile:\n", - " resp = requests.get('https://dataserv.ub.tum.de/s/m1615239/download?path=%2F&files=dfp-data-400.npz', verify=False)\n", - " datafile.write(resp.content)\n", - "else: \n", - " # alternative: load from google drive (upload there beforehand):\n", - " from google.colab import drive\n", - " drive.mount('/content/gdrive')\n", - " dir = \"./gdrive/My Drive/\"\n", - "\n", - "npfile=np.load(dir+'data-airfoils.npz')\n", - " \n", - "print(\"Loaded data, {} training, {} validation samples\".format(len(npfile[\"inputs\"]),len(npfile[\"vinputs\"])))\n", - "\n", - "print(\"Size of the inputs array: \"+format(npfile[\"inputs\"].shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you run this notebook in colab, the `else` statement above (which is deactivated by default) might be interesting for you: instead of downloading the training data anew every time, you can manually download it once and store it in your google drive. We assume it's stored in the root directory as `data-airfoils.npz`. Afterwards, you can use the code above to load the file from your google drive, which is typically much faster. This is highly recommended if you want to experiment more extensively via colab." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RY1F4kdWPLNG" - }, - "source": [ - "## RANS training data\n", - "\n", - "Now we have some training data. In general it's very important to understand the data we're working with as much as possible (for any ML task the _garbage-in-gargabe-out_ principle definitely holds). We should at least understand the data in terms of dimensions and rough statistics, but ideally also in terms of content. Otherwise we'll have a very hard time interpreting the results of a training run. And despite all the DL magic: if you can't make out any patterns in your data, NNs surely won't find any useful ones.\n", - "\n", - "Hence, let's look at one of the training samples... The following is just some helper code to show images side by side." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 353 - }, - "id": "6y6YGxMeNECD", - "outputId": "91a5b3c7-4b87-4e31-d6e8-105d09d08f7e" - }, - "outputs": [ - { - "data": { - "image/png": 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" + "cell_type": "markdown", + "metadata": { + "id": "qT_RWmTEugu9" + }, + "source": [ + "# Supervised training for RANS flows around airfoils\n", + "\n", + "## Overview \n", + "\n", + "For this example of supervised training\n", + "we have a turbulent airflow around wing profiles, and we'd like to know the average motion\n", + "and pressure distribution around this airfoil for different Reynolds numbers and angles of attack.\n", + "Thus, given an airfoil shape, Reynolds numbers, and angle of attack, we'd like to obtain\n", + "a velocity field and a pressure field around the airfoil.\n", + "\n", + "This is classically approximated with _Reynolds-Averaged Navier Stokes_ (RANS) models, and this\n", + "setting is still one of the most widely used applications of Navier-Stokes solver in industry.\n", + "However, instead of relying on traditional numerical methods to solve the RANS equations,\n", + "we now aim for training a surrogate model via a neural network that completely bypasses the numerical solver,\n", + "and produces the solution in terms of velocity and pressure.\n", + "[[run in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/supervised-airfoils.ipynb)\n" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pylab\n", - "\n", - "# helper to show three target channels: normalized, with colormap, side by side\n", - "def showSbs(a1,a2, stats=False, bottom=\"NN Output\", top=\"Reference\", title=None): \n", - " c=[]\n", - " for i in range(3):\n", - " b = np.flipud( np.concatenate((a2[i],a1[i]),axis=1).transpose())\n", - " min, mean, max = np.min(b), np.mean(b), np.max(b); \n", - " if stats: print(\"Stats %d: \"%i + format([min,mean,max]))\n", - " b -= min; b /= (max-min)\n", - " c.append(b)\n", - " fig, axes = pylab.subplots(1, 1, figsize=(16, 5))\n", - " axes.set_xticks([]); axes.set_yticks([]); \n", - " im = axes.imshow(np.concatenate(c,axis=1), origin='upper', cmap='magma')\n", - "\n", - " pylab.colorbar(im); pylab.xlabel('p, ux, uy'); pylab.ylabel('%s %s'%(bottom,top))\n", - " if title is not None: pylab.title(title)\n", - "\n", - "NUM=72\n", - "showSbs(npfile[\"inputs\"][NUM],npfile[\"targets\"][NUM], stats=False, bottom=\"Target Output\", top=\"Inputs\", title=\"3 inputs are shown at the top (mask, in-ux, in-uy), with the 3 output channels (p,ux,uy) at the bottom\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TVHhr8zCPUfN" - }, - "source": [ - "Next, let's define a small helper class `DfpDataset` to organize inputs and targets. We'll transfer the corresponding data to the pytorch `DataLoader` class. \n", - "\n", - "We also set up some globals to control training parameters, maybe most importantly: the learning rate `LR`, i.e. $\\eta$ from the previous setions. When your training run doesn't converge this is the first parameter to experiment with.\n", - "\n", - "Here, we'll keep it relatively small throughout. (Using _learning rate decay_ would be better, i.e. potentially give an improved convergence, but is omitted here for clarity.) " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "BGN4GqxkIueM", - "outputId": "b97ce299-8c12-402a-d90a-c583224c8971" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training & validation batches: 32 , 8\n" - ] - } - ], - "source": [ - "# some global training constants\n", - "\n", - "# number of training epochs\n", - "EPOCHS = 100\n", - "# batch size\n", - "BATCH_SIZE = 10\n", - "# learning rate\n", - "LR = 0.00002\n", - "\n", - "class DfpDataset():\n", - " def __init__(self, inputs,targets): \n", - " self.inputs = inputs\n", - " self.targets = targets\n", - "\n", - " def __len__(self):\n", - " return len(self.inputs)\n", - "\n", - " def __getitem__(self, idx):\n", - " return self.inputs[idx], self.targets[idx]\n", - "\n", - "tdata = DfpDataset(npfile[\"inputs\"],npfile[\"targets\"])\n", - "vdata = DfpDataset(npfile[\"vinputs\"],npfile[\"vtargets\"])\n", - "\n", - "trainLoader = torch.utils.data.DataLoader(tdata, batch_size=BATCH_SIZE, shuffle=True , drop_last=True) \n", - "valiLoader = torch.utils.data.DataLoader(vdata, batch_size=BATCH_SIZE, shuffle=False, drop_last=True) \n", - "\n", - "print(\"Training & validation batches: {} , {}\".format(len(trainLoader),len(valiLoader) ))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9ys7KZu_P5NB" - }, - "source": [ - "## Network setup\n", - "\n", - "Now we can set up the architecture of our neural network, we'll use a fully convolutional U-net. This is a widely used architecture that uses a stack of convolutions across different spatial resolutions. The main deviation from a regular conv-net is to introduce _skip connection_ from the encoder to the decoder part. This ensures that no information is lost during feature extraction. (Note that this only works if the network is to be used as a whole. It doesn't work in situations where we'd, e.g., want to use the decoder as a standalone component.)\n", - "\n", - "Here's a overview of the architecure:\n", - "\n", - "![An overview of the U-net we're using for this learning task](resources/supervised-airfoils-unet.jpg)\n", - "\n", - "First, we'll define a helper to set up a convolutional block in the network, `blockUNet`. Note, we don't use any pooling! Instead we use strides and transpose convolutions (these need to be symmetric for the decoder part, i.e. have an uneven kernel size), following [best practices](https://distill.pub/2016/deconv-checkerboard/). The full pytroch neural network is managed via the `DfpNet` class." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "PVNjz4FjGhdY" - }, - "outputs": [], - "source": [ - "import os, sys, random\n", - "import numpy as np\n", - "\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torch.autograd \n", - "import torch.utils.data \n", - "\n", - "def blockUNet(in_c, out_c, name, size=4, pad=1, transposed=False, bn=True, activation=True, relu=True, dropout=0. ):\n", - " block = nn.Sequential()\n", - "\n", - " if not transposed:\n", - " block.add_module('%s_conv' % name, nn.Conv2d(in_c, out_c, kernel_size=size, stride=2, padding=pad, bias=True))\n", - " else:\n", - " block.add_module('%s_upsam' % name, nn.Upsample(scale_factor=2, mode='bilinear'))\n", - " # reduce kernel size by one for the upsampling (ie decoder part)\n", - " block.add_module('%s_tconv' % name, nn.Conv2d(in_c, out_c, kernel_size=(size-1), stride=1, padding=pad, bias=True))\n", - "\n", - " if bn:\n", - " block.add_module('%s_bn' % name, nn.BatchNorm2d(out_c))\n", - " if dropout>0.:\n", - " block.add_module('%s_dropout' % name, nn.Dropout2d( dropout, inplace=True))\n", - "\n", - " if activation:\n", - " if relu:\n", - " block.add_module('%s_relu' % name, nn.ReLU(inplace=True))\n", - " else:\n", - " block.add_module('%s_leakyrelu' % name, nn.LeakyReLU(0.2, inplace=True))\n", - "\n", - " return block\n", - " \n", - "class DfpNet(nn.Module):\n", - " def __init__(self, channelExponent=6, dropout=0.):\n", - " super(DfpNet, self).__init__()\n", - " channels = int(2 ** channelExponent + 0.5)\n", - "\n", - " self.layer1 = blockUNet(3 , channels*1, 'enc_layer1', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer2 = blockUNet(channels , channels*2, 'enc_layer2', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer3 = blockUNet(channels*2, channels*2, 'enc_layer3', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer4 = blockUNet(channels*2, channels*4, 'enc_layer4', transposed=False, bn=True, relu=False, dropout=dropout )\n", - " self.layer5 = blockUNet(channels*4, channels*8, 'enc_layer5', transposed=False, bn=True, relu=False, dropout=dropout ) \n", - " self.layer6 = blockUNet(channels*8, channels*8, 'enc_layer6', transposed=False, bn=True, relu=False, dropout=dropout , size=2,pad=0)\n", - " self.layer7 = blockUNet(channels*8, channels*8, 'enc_layer7', transposed=False, bn=True, relu=False, dropout=dropout , size=2,pad=0)\n", - " \n", - " # note, kernel size is internally reduced by one for the decoder part\n", - " self.dlayer7 = blockUNet(channels*8, channels*8, 'dec_layer7', transposed=True, bn=True, relu=True, dropout=dropout , size=2,pad=0)\n", - " self.dlayer6 = blockUNet(channels*16,channels*8, 'dec_layer6', transposed=True, bn=True, relu=True, dropout=dropout , size=2,pad=0)\n", - " self.dlayer5 = blockUNet(channels*16,channels*4, 'dec_layer5', transposed=True, bn=True, relu=True, dropout=dropout ) \n", - " self.dlayer4 = blockUNet(channels*8, channels*2, 'dec_layer4', transposed=True, bn=True, relu=True, dropout=dropout )\n", - " self.dlayer3 = blockUNet(channels*4, channels*2, 'dec_layer3', transposed=True, bn=True, relu=True, dropout=dropout )\n", - " self.dlayer2 = blockUNet(channels*4, channels , 'dec_layer2', transposed=True, bn=True, relu=True, dropout=dropout )\n", - " self.dlayer1 = blockUNet(channels*2, 3 , 'dec_layer1', transposed=True, bn=False, activation=False, dropout=dropout )\n", - "\n", - " def forward(self, x):\n", - " # note, this Unet stack could be allocated with a loop, of course... \n", - " out1 = self.layer1(x)\n", - " out2 = self.layer2(out1)\n", - " out3 = self.layer3(out2)\n", - " out4 = self.layer4(out3)\n", - " out5 = self.layer5(out4)\n", - " out6 = self.layer6(out5)\n", - " out7 = self.layer7(out6)\n", - " # ... bottleneck ...\n", - " dout6 = self.dlayer7(out7)\n", - " dout6_out6 = torch.cat([dout6, out6], 1)\n", - " dout6 = self.dlayer6(dout6_out6)\n", - " dout6_out5 = torch.cat([dout6, out5], 1)\n", - " dout5 = self.dlayer5(dout6_out5)\n", - " dout5_out4 = torch.cat([dout5, out4], 1)\n", - " dout4 = self.dlayer4(dout5_out4)\n", - " dout4_out3 = torch.cat([dout4, out3], 1)\n", - " dout3 = self.dlayer3(dout4_out3)\n", - " dout3_out2 = torch.cat([dout3, out2], 1)\n", - " dout2 = self.dlayer2(dout3_out2)\n", - " dout2_out1 = torch.cat([dout2, out1], 1)\n", - " dout1 = self.dlayer1(dout2_out1)\n", - " return dout1\n", - "\n", - "def weights_init(m):\n", - " classname = m.__class__.__name__\n", - " if classname.find('Conv') != -1:\n", - " m.weight.data.normal_(0.0, 0.02)\n", - " elif classname.find('BatchNorm') != -1:\n", - " m.weight.data.normal_(1.0, 0.02)\n", - " m.bias.data.fill_(0)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QAl3VgKVQSI3" - }, - "source": [ - "Next, we can initialize an instance of the `DfpNet`.\n", - "\n", - "Below, the `EXPO` parameter here controls the exponent for the feature maps of our Unet: this directly scales the network size (3 gives a network with ca. 150k parameters). This is relatively small for a generative NN with $3 \\times 128^2 = \\text{ca. }49k$ outputs, but yields fast training times and prevents overfitting given the relatively small data set we're using here. Hence it's a good starting point." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "N1uV8k5VIoqT", - "outputId": "ceb8fb79-6154-423a-9d17-d6e4dd0c2718" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Trainable params: 147363 -> crucial! always keep in view... \n" - ] - } - ], - "source": [ - "# channel exponent to control network size\n", - "EXPO = 3\n", - "\n", - "# setup network\n", - "net = DfpNet(channelExponent=EXPO)\n", - "#print(net) # to double check the details...\n", - "\n", - "nn_parameters = filter(lambda p: p.requires_grad, net.parameters())\n", - "params = sum([np.prod(p.size()) for p in nn_parameters])\n", - "\n", - "# crucial parameter to keep in view: how many parameters do we have?\n", - "print(\"Trainable params: {} -> crucial! always keep in view... \".format(params)) \n", - "\n", - "net.apply(weights_init)\n", - "\n", - "criterionL1 = nn.L1Loss()\n", - "optimizerG = optim.Adam(net.parameters(), lr=LR, betas=(0.5, 0.999), weight_decay=0.0)\n", - "\n", - "targets = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n", - "inputs = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UNjBAvfWJMTR" - }, - "source": [ - "With an exponent of 3, this network has 147555 trainable parameters. As the subtle hint in the print statement indicates, this is a crucial number to always have in view when training NNs. It's easy to change settings, and get a network that has millions of parameters, and as a result probably all kinds of convergence and overfitting problems. The number of parameters definitely has to be matched with the amount of training data, and should also scale with the depth of the network. How these three relate to each other exactly is problem dependent, though." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ErLQ6UYlQT8-" - }, - "source": [ - "## Training\n", - "\n", - "Finally, we can train the NN. This step can take a while, as the training runs over all 320 samples 100 times, and continually evaluates the validation samples to keep track of how well the current state of the NN is doing." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kkOYvwxqKa8n", - "outputId": "a973331c-9e8f-401f-dfd7-9b7177faca0f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing network, loading & skipping training\n" - ] - } - ], - "source": [ - "history_L1 = []\n", - "history_L1val = []\n", - "\n", - "if os.path.isfile(\"network\"):\n", - " print(\"Found existing network, loading & skipping training\")\n", - " net.load_state_dict(torch.load(\"network\")) # optionally, load existing network\n", - "\n", - "else:\n", - " print(\"Training from scratch\")\n", - " for epoch in range(EPOCHS):\n", - " net.train()\n", - " L1_accum = 0.0\n", - " for i, traindata in enumerate(trainLoader, 0):\n", - " inputs_curr, targets_curr = traindata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", - "\n", - " net.zero_grad()\n", - " gen_out = net(inputs)\n", - "\n", - " lossL1 = criterionL1(gen_out, targets)\n", - " lossL1.backward()\n", - " optimizerG.step()\n", - " L1_accum += lossL1.item()\n", - "\n", - " # validation\n", - " net.eval()\n", - " L1val_accum = 0.0\n", - " for i, validata in enumerate(valiLoader, 0):\n", - " inputs_curr, targets_curr = validata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", - "\n", - " outputs = net(inputs)\n", - " outputs_curr = outputs.data.cpu().numpy()\n", - "\n", - " lossL1val = criterionL1(outputs, targets)\n", - " L1val_accum += lossL1val.item()\n", - "\n", - " # data for graph plotting\n", - " history_L1.append( L1_accum / len(trainLoader) )\n", - " history_L1val.append( L1val_accum / len(valiLoader) )\n", - "\n", - " if epoch<3 or epoch%20==0:\n", - " print( \"Epoch: {}, L1 train: {:7.5f}, L1 vali: {:7.5f}\".format(epoch, history_L1[-1], history_L1val[-1]) )\n", - "\n", - " torch.save(net.state_dict(), \"network\" )\n", - " print(\"Training done, saved network\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4KuUpJsSL3Jv" - }, - "source": [ - "The NN is finally trained! The losses should have nicely gone down in terms of absolute values: With the standard settings from an initial value of around 0.2 for the validation loss, to ca. 0.02 after 100 epochs. \n", - "\n", - "Let's look at the graphs to get some intuition for how the training progressed over time. This is typically important to identify longer-term trends in the training. In practice it's tricky to spot whether the overall trend of 100 or so noisy numbers in a command line log is going slightly up or down - this is much easier to spot in a visualization." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 286 - }, - "id": "Hlki3bU8JMTT", - "outputId": "2a36d013-5af2-4580-f782-86b65f6f8c11" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "2ZaC0_MdJMTH" + }, + "source": [ + "## Formulation\n", + "\n", + "\n", + "With the supervised formulation from {doc}`supervised`, our learning task is pretty straight-forward, and can be written as \n", + "\n", + "$$\\begin{aligned}\n", + "\\text{arg min}_{\\theta} \\sum_i ( f(x_i ; \\theta)-y^*_i )^2 ,\n", + "\\end{aligned}$$\n", + "\n", + "where $x$ and $y^*$ each consist of a set of physical fields,\n", + "and the index $i$ evaluates the difference across all discretization points in our data sets.\n", + "\n", + "The goal is to infer velocity $\\mathbf{u} = u_x,u_y$ and a pressure field $p$ in a computational domain $\\Omega$\n", + "around the airfoil in the center of $\\Omega$. \n", + "$u_x,u_y$ and $p$ each have a dimension of $128^2$.\n", + "As inputs we have the Reynolds number $\\text{Re} \\in \\mathbb{R}$, the angle of attack\n", + "$\\alpha \\in \\mathbb{R}$, and the airfoil shape $\\mathbf{s}$ encoded as a rasterized grid with $128^2$.\n", + "Both constant, scalar inputs $\\text{Re}$ and $\\alpha$ are likewise extended to a size of $128^2$.\n", + "Thus, put together, both input and output have the same dimensions: $x,y^* \\in \\mathbb{R}^{3\\times128\\times128}$.\n", + "This is exactly what we'll specify as input and output dimensions for the NN below.\n", + "\n", + "A point to keep in mind here is that our quantities of interest in $y^*$ contain three different physical fields. While the two velocity components are quite similar in spirit, the pressure field typically has a different behavior with an approximately squared scaling with respect to the velocity (cf. [Bernoulli](https://en.wikipedia.org/wiki/Bernoulli%27s_principle)). This implies that we need to be careful with simple summations (as in the minimization problem above), and that we should take care to normalize the data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ewKoLbFCJMTJ" + }, + "source": [ + "## Code coming up...\n", + "\n", + "Let's get started with the implementation. Note that we'll skip the data generation process here. The code below is adapted from {cite}`thuerey2020dfp` and [this codebase](https://github.com/thunil/Deep-Flow-Prediction), which you can check out for details. Here, we'll simply download a small set of training data generated with a Spalart-Almaras RANS simulation in [OpenFOAM](https://openfoam.org/)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JwZudtWauiGa", + "outputId": "44bb304e-5839-41a1-9cb0-2f52a41f0d90" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Torch version 1.11.0+cu113\n", + "Downloading training data (300MB), this can take a few minutes the first time...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n", + " InsecureRequestWarning)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loaded data, 320 training, 80 validation samples\n", + "Size of the inputs array: (320, 3, 128, 128)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import os.path, random\n", + "import torch\n", + "from torch.utils.data import Dataset\n", + "print(\"Torch version {}\".format(torch.__version__))\n", + "\n", + "# get training data\n", + "dir = \"./\"\n", + "if True:\n", + " # download\n", + " if not os.path.isfile('data-airfoils.npz'):\n", + " import requests\n", + " print(\"Downloading training data (300MB), this can take a few minutes the first time...\")\n", + " with open(\"data-airfoils.npz\", 'wb') as datafile:\n", + " resp = requests.get('https://dataserv.ub.tum.de/s/m1615239/download?path=%2F&files=dfp-data-400.npz', verify=False)\n", + " datafile.write(resp.content)\n", + "else: \n", + " # alternative: load from google drive (upload there beforehand):\n", + " from google.colab import drive\n", + " drive.mount('/content/gdrive')\n", + " dir = \"./gdrive/My Drive/\"\n", + "\n", + "npfile=np.load(dir+'data-airfoils.npz')\n", + " \n", + "print(\"Loaded data, {} training, {} validation samples\".format(len(npfile[\"inputs\"]),len(npfile[\"vinputs\"])))\n", + "\n", + "print(\"Size of the inputs array: \"+format(npfile[\"inputs\"].shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u488C6ybnMCN" + }, + "source": [ + "If you run this notebook in colab, the `else` statement above (which is deactivated by default) might be interesting for you: instead of downloading the training data anew every time, you can manually download it once and store it in your google drive. We assume it's stored in the root directory as `data-airfoils.npz`. Afterwards, you can use the code above to load the file from your google drive, which is typically much faster. This is highly recommended if you want to experiment more extensively via colab." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RY1F4kdWPLNG" + }, + "source": [ + "## RANS training data\n", + "\n", + "Now we have some training data. In general it's very important to understand the data we're working with as much as possible (for any ML task the _garbage-in-gargabe-out_ principle definitely holds). We should at least understand the data in terms of dimensions and rough statistics, but ideally also in terms of content. Otherwise we'll have a very hard time interpreting the results of a training run. And despite all the DL magic: if you can't make out any patterns in your data, NNs surely won't find any useful ones.\n", + "\n", + "Hence, let's look at one of the training samples... The following is just some helper code to show images side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 353 + }, + "id": "6y6YGxMeNECD", + "outputId": "20e4ed46-2ec0-4144-d9da-e49a9f6aa6a2" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import pylab\n", + "\n", + "# helper to show three target channels: normalized, with colormap, side by side\n", + "def showSbs(a1,a2, stats=False, bottom=\"NN Output\", top=\"Reference\", title=None): \n", + " c=[]\n", + " for i in range(3):\n", + " b = np.flipud( np.concatenate((a2[i],a1[i]),axis=1).transpose())\n", + " min, mean, max = np.min(b), np.mean(b), np.max(b); \n", + " if stats: print(\"Stats %d: \"%i + format([min,mean,max]))\n", + " b -= min; b /= (max-min)\n", + " c.append(b)\n", + " fig, axes = pylab.subplots(1, 1, figsize=(16, 5))\n", + " axes.set_xticks([]); axes.set_yticks([]); \n", + " im = axes.imshow(np.concatenate(c,axis=1), origin='upper', cmap='magma')\n", + "\n", + " pylab.colorbar(im); pylab.xlabel('p, ux, uy'); pylab.ylabel('%s %s'%(bottom,top))\n", + " if title is not None: pylab.title(title)\n", + "\n", + "NUM=72\n", + "showSbs(npfile[\"inputs\"][NUM],npfile[\"targets\"][NUM], stats=False, bottom=\"Target Output\", top=\"Inputs\", title=\"3 inputs are shown at the top (mask, in-ux, in-uy), with the 3 output channels (p,ux,uy) at the bottom\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TVHhr8zCPUfN" + }, + "source": [ + "Next, let's define a small helper class `DfpDataset` to organize inputs and targets. We'll transfer the corresponding data to the pytorch `DataLoader` class. \n", + "\n", + "We also set up some globals to control training parameters, maybe most importantly: the learning rate `LR`, i.e. $\\eta$ from the previous setions. When your training run doesn't converge this is the first parameter to experiment with.\n", + "\n", + "Here, we'll keep it relatively small throughout. (Using _learning rate decay_ would be better, i.e. potentially give an improved convergence, but is omitted here for clarity.) " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BGN4GqxkIueM", + "outputId": "edafc7f9-9f88-4d6a-ebea-77500412ef1e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training & validation batches: 32 , 8\n" + ] + } + ], + "source": [ + "# some global training constants\n", + "\n", + "# number of training epochs\n", + "EPOCHS = 100\n", + "# batch size\n", + "BATCH_SIZE = 10\n", + "# learning rate\n", + "LR = 0.00002\n", + "\n", + "class DfpDataset():\n", + " def __init__(self, inputs,targets): \n", + " self.inputs = inputs\n", + " self.targets = targets\n", + "\n", + " def __len__(self):\n", + " return len(self.inputs)\n", + "\n", + " def __getitem__(self, idx):\n", + " return self.inputs[idx], self.targets[idx]\n", + "\n", + "tdata = DfpDataset(npfile[\"inputs\"],npfile[\"targets\"])\n", + "vdata = DfpDataset(npfile[\"vinputs\"],npfile[\"vtargets\"])\n", + "\n", + "trainLoader = torch.utils.data.DataLoader(tdata, batch_size=BATCH_SIZE, shuffle=True , drop_last=True) \n", + "valiLoader = torch.utils.data.DataLoader(vdata, batch_size=BATCH_SIZE, shuffle=False, drop_last=True) \n", + "\n", + "print(\"Training & validation batches: {} , {}\".format(len(trainLoader),len(valiLoader) ))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9ys7KZu_P5NB" + }, + "source": [ + "## Network setup\n", + "\n", + "Now we can set up the architecture of our neural network, we'll use a fully convolutional U-net. This is a widely used architecture that uses a stack of convolutions across different spatial resolutions. The main deviation from a regular conv-net is to introduce _skip connection_ from the encoder to the decoder part. This ensures that no information is lost during feature extraction. (Note that this only works if the network is to be used as a whole. It doesn't work in situations where we'd, e.g., want to use the decoder as a standalone component.)\n", + "\n", + "Here's a overview of the architecure:\n", + "\n", + "![An overview of the U-net we're using for this learning task](https://github.com/tum-pbs/pbdl-book/blob/master/resources/supervised-airfoils-unet.jpg?raw=1)\n", + "\n", + "First, we'll define a helper to set up a convolutional block in the network, `blockUNet`. Note, we don't use any pooling! Instead we use strides and transpose convolutions (these need to be symmetric for the decoder part, i.e. have an uneven kernel size), following [best practices](https://distill.pub/2016/deconv-checkerboard/). The full pytroch neural network is managed via the `DfpNet` class." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "PVNjz4FjGhdY" + }, + "outputs": [], + "source": [ + "import os, sys, random\n", + "import numpy as np\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.autograd \n", + "import torch.utils.data \n", + "\n", + "def blockUNet(in_c, out_c, name, size=4, pad=1, transposed=False, bn=True, activation=True, relu=True, dropout=0. ):\n", + " block = nn.Sequential()\n", + "\n", + " if not transposed:\n", + " block.add_module('%s_conv' % name, nn.Conv2d(in_c, out_c, kernel_size=size, stride=2, padding=pad, bias=True))\n", + " else:\n", + " block.add_module('%s_upsam' % name, nn.Upsample(scale_factor=2, mode='bilinear'))\n", + " # reduce kernel size by one for the upsampling (ie decoder part)\n", + " block.add_module('%s_tconv' % name, nn.Conv2d(in_c, out_c, kernel_size=(size-1), stride=1, padding=pad, bias=True))\n", + "\n", + " if bn:\n", + " block.add_module('%s_bn' % name, nn.BatchNorm2d(out_c))\n", + " if dropout>0.:\n", + " block.add_module('%s_dropout' % name, nn.Dropout2d( dropout, inplace=True))\n", + "\n", + " if activation:\n", + " if relu:\n", + " block.add_module('%s_relu' % name, nn.ReLU(inplace=True))\n", + " else:\n", + " block.add_module('%s_leakyrelu' % name, nn.LeakyReLU(0.2, inplace=True))\n", + "\n", + " return block\n", + " \n", + "class DfpNet(nn.Module):\n", + " def __init__(self, channelExponent=6, dropout=0.):\n", + " super(DfpNet, self).__init__()\n", + " channels = int(2 ** channelExponent + 0.5)\n", + "\n", + " self.layer1 = blockUNet(3 , channels*1, 'enc_layer1', transposed=False, bn=True, relu=False, dropout=dropout )\n", + " self.layer2 = blockUNet(channels , channels*2, 'enc_layer2', transposed=False, bn=True, relu=False, dropout=dropout )\n", + " self.layer3 = blockUNet(channels*2, channels*2, 'enc_layer3', transposed=False, bn=True, relu=False, dropout=dropout )\n", + " self.layer4 = blockUNet(channels*2, channels*4, 'enc_layer4', transposed=False, bn=True, relu=False, dropout=dropout )\n", + " self.layer5 = blockUNet(channels*4, channels*8, 'enc_layer5', transposed=False, bn=True, relu=False, dropout=dropout ) \n", + " self.layer6 = blockUNet(channels*8, channels*8, 'enc_layer6', transposed=False, bn=True, relu=False, dropout=dropout , size=2,pad=0)\n", + " self.layer7 = blockUNet(channels*8, channels*8, 'enc_layer7', transposed=False, bn=True, relu=False, dropout=dropout , size=2,pad=0)\n", + " \n", + " # note, kernel size is internally reduced by one for the decoder part\n", + " self.dlayer7 = blockUNet(channels*8, channels*8, 'dec_layer7', transposed=True, bn=True, relu=True, dropout=dropout , size=2,pad=0)\n", + " self.dlayer6 = blockUNet(channels*16,channels*8, 'dec_layer6', transposed=True, bn=True, relu=True, dropout=dropout , size=2,pad=0)\n", + " self.dlayer5 = blockUNet(channels*16,channels*4, 'dec_layer5', transposed=True, bn=True, relu=True, dropout=dropout ) \n", + " self.dlayer4 = blockUNet(channels*8, channels*2, 'dec_layer4', transposed=True, bn=True, relu=True, dropout=dropout )\n", + " self.dlayer3 = blockUNet(channels*4, channels*2, 'dec_layer3', transposed=True, bn=True, relu=True, dropout=dropout )\n", + " self.dlayer2 = blockUNet(channels*4, channels , 'dec_layer2', transposed=True, bn=True, relu=True, dropout=dropout )\n", + " self.dlayer1 = blockUNet(channels*2, 3 , 'dec_layer1', transposed=True, bn=False, activation=False, dropout=dropout )\n", + "\n", + " def forward(self, x):\n", + " # note, this Unet stack could be allocated with a loop, of course... \n", + " out1 = self.layer1(x)\n", + " out2 = self.layer2(out1)\n", + " out3 = self.layer3(out2)\n", + " out4 = self.layer4(out3)\n", + " out5 = self.layer5(out4)\n", + " out6 = self.layer6(out5)\n", + " out7 = self.layer7(out6)\n", + " # ... bottleneck ...\n", + " dout6 = self.dlayer7(out7)\n", + " dout6_out6 = torch.cat([dout6, out6], 1)\n", + " dout6 = self.dlayer6(dout6_out6)\n", + " dout6_out5 = torch.cat([dout6, out5], 1)\n", + " dout5 = self.dlayer5(dout6_out5)\n", + " dout5_out4 = torch.cat([dout5, out4], 1)\n", + " dout4 = self.dlayer4(dout5_out4)\n", + " dout4_out3 = torch.cat([dout4, out3], 1)\n", + " dout3 = self.dlayer3(dout4_out3)\n", + " dout3_out2 = torch.cat([dout3, out2], 1)\n", + " dout2 = self.dlayer2(dout3_out2)\n", + " dout2_out1 = torch.cat([dout2, out1], 1)\n", + " dout1 = self.dlayer1(dout2_out1)\n", + " return dout1\n", + "\n", + "def weights_init(m):\n", + " classname = m.__class__.__name__\n", + " if classname.find('Conv') != -1:\n", + " m.weight.data.normal_(0.0, 0.02)\n", + " elif classname.find('BatchNorm') != -1:\n", + " m.weight.data.normal_(1.0, 0.02)\n", + " m.bias.data.fill_(0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QAl3VgKVQSI3" + }, + "source": [ + "Next, we can initialize an instance of the `DfpNet`.\n", + "\n", + "Below, the `EXPO` parameter here controls the exponent for the feature maps of our Unet: this directly scales the network size (3 gives a network with ca. 150k parameters). This is relatively small for a generative NN with $3 \\times 128^2 = \\text{ca. }49k$ outputs, but yields fast training times and prevents overfitting given the relatively small data set we're using here. Hence it's a good starting point." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N1uV8k5VIoqT", + "outputId": "f91c9beb-5ca9-4516-ae74-7ba87b2a2b0e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Trainable params: 147363 -> crucial! always keep in view... \n" + ] + } + ], + "source": [ + "# channel exponent to control network size\n", + "EXPO = 3\n", + "\n", + "# setup network\n", + "net = DfpNet(channelExponent=EXPO)\n", + "#print(net) # to double check the details...\n", + "\n", + "nn_parameters = filter(lambda p: p.requires_grad, net.parameters())\n", + "params = sum([np.prod(p.size()) for p in nn_parameters])\n", + "\n", + "# crucial parameter to keep in view: how many parameters do we have?\n", + "print(\"Trainable params: {} -> crucial! always keep in view... \".format(params)) \n", + "\n", + "net.apply(weights_init)\n", + "\n", + "criterionL1 = nn.L1Loss()\n", + "optimizerG = optim.Adam(net.parameters(), lr=LR, betas=(0.5, 0.999), weight_decay=0.0)\n", + "\n", + "targets = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n", + "inputs = torch.autograd.Variable(torch.FloatTensor(BATCH_SIZE, 3, 128, 128))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UNjBAvfWJMTR" + }, + "source": [ + "With an exponent of 3, this network has 147555 trainable parameters. As the subtle hint in the print statement indicates, this is a crucial number to always have in view when training NNs. It's easy to change settings, and get a network that has millions of parameters, and as a result probably all kinds of convergence and overfitting problems. The number of parameters definitely has to be matched with the amount of training data, and should also scale with the depth of the network. How these three relate to each other exactly is problem dependent, though." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ErLQ6UYlQT8-" + }, + "source": [ + "## Training\n", + "\n", + "Finally, we can train the NN. This step can take a while, as the training runs over all 320 samples 100 times, and continually evaluates the validation samples to keep track of how well the current state of the NN is doing." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kkOYvwxqKa8n", + "outputId": "5daf4281-1151-4fc4-fa3a-74a9ff8435d9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training from scratch\n", + "Epoch: 0, L1 train: 0.29219, L1 vali: 0.23295\n", + "Epoch: 1, L1 train: 0.25406, L1 vali: 0.22507\n", + "Epoch: 2, L1 train: 0.22487, L1 vali: 0.21019\n", + "Epoch: 20, L1 train: 0.05228, L1 vali: 0.04134\n", + "Epoch: 40, L1 train: 0.03730, L1 vali: 0.03020\n", + "Epoch: 60, L1 train: 0.03236, L1 vali: 0.02523\n", + "Epoch: 80, L1 train: 0.03364, L1 vali: 0.02302\n", + "Training done, saved network\n" + ] + } + ], + "source": [ + "history_L1 = []\n", + "history_L1val = []\n", + "\n", + "if os.path.isfile(\"network\"):\n", + " print(\"Found existing network, loading & skipping training\")\n", + " net.load_state_dict(torch.load(\"network\")) # optionally, load existing network\n", + "\n", + "else:\n", + " print(\"Training from scratch\")\n", + " for epoch in range(EPOCHS):\n", + " net.train()\n", + " L1_accum = 0.0\n", + " for i, traindata in enumerate(trainLoader, 0):\n", + " inputs_curr, targets_curr = traindata\n", + " inputs.data.copy_(inputs_curr.float())\n", + " targets.data.copy_(targets_curr.float())\n", + "\n", + " net.zero_grad()\n", + " gen_out = net(inputs)\n", + "\n", + " lossL1 = criterionL1(gen_out, targets)\n", + " lossL1.backward()\n", + " optimizerG.step()\n", + " L1_accum += lossL1.item()\n", + "\n", + " # validation\n", + " net.eval()\n", + " L1val_accum = 0.0\n", + " for i, validata in enumerate(valiLoader, 0):\n", + " inputs_curr, targets_curr = validata\n", + " inputs.data.copy_(inputs_curr.float())\n", + " targets.data.copy_(targets_curr.float())\n", + "\n", + " outputs = net(inputs)\n", + " outputs_curr = outputs.data.cpu().numpy()\n", + "\n", + " lossL1val = criterionL1(outputs, targets)\n", + " L1val_accum += lossL1val.item()\n", + "\n", + " # data for graph plotting\n", + " history_L1.append( L1_accum / len(trainLoader) )\n", + " history_L1val.append( L1val_accum / len(valiLoader) )\n", + "\n", + " if epoch<3 or epoch%20==0:\n", + " print( \"Epoch: {}, L1 train: {:7.5f}, L1 vali: {:7.5f}\".format(epoch, history_L1[-1], history_L1val[-1]) )\n", + "\n", + " torch.save(net.state_dict(), \"network\" )\n", + " print(\"Training done, saved network\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4KuUpJsSL3Jv" + }, + "source": [ + "The NN is finally trained! The losses should have nicely gone down in terms of absolute values: With the standard settings from an initial value of around 0.2 for the validation loss, to ca. 0.02 after 100 epochs. \n", + "\n", + "Let's look at the graphs to get some intuition for how the training progressed over time. This is typically important to identify longer-term trends in the training. In practice it's tricky to spot whether the overall trend of 100 or so noisy numbers in a command line log is going slightly up or down - this is much easier to spot in a visualization." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "id": "Hlki3bU8JMTT", + "outputId": "fa4b4911-85f2-478d-87e2-44b90bf5b83b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "l1train = np.asarray(history_L1)\n", + "l1vali = np.asarray(history_L1val)\n", + "\n", + "plt.plot(np.arange(l1train.shape[0]),l1train,'b',label='Training loss')\n", + "plt.plot(np.arange(l1vali.shape[0] ),l1vali ,'g',label='Validation loss')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Vj1E9ZdJMTU" + }, + "source": [ + "You should see a curve that goes down for ca. 40 epochs, and then starts to flatten out. In the last part, it's still slowly decreasing, and most importantly, the validation loss is not increasing. This would be a certain sign of overfitting, and something that we should avoid. (Try decreasing the amount of training data artificially, then you should be able to intentionally cause overfitting.)\n", + "\n", + "## Training progress and validation\n", + "\n", + "If you look closely at this graph, you should spot something peculiar:\n", + "_Why is the validation loss lower than the training loss_?\n", + "The data is similar to the training data of course, but in a way it's slightly \"tougher\", because the network certainly never received any validation samples during training. It is natural that the validation loss slightly deviates from the training loss, but how can the L1 loss be _lower_ for these inputs?\n", + "\n", + "This is a subtlety of the training loop above: it runs a training step first, and the loss for each point in the graph is measured with the evolving state of the network in an epoch. The network is updated, and afterwards runs through the validation samples. Thus all validation samples are using a state that is definitely different (and hopefully a bit better) than the initial states of the epoch. Hence, the validation loss can be slightly lower.\n", + "\n", + "A general word of caution here: never evaluate your network with training data! That won't tell you much because overfitting is a very common problem. At least use data the network hasn't seen before, i.e. validation data, and if that looks good, try some more different (at least slightly out-of-distribution) inputs, i.e., _test data_. The next cell runs the trained network over the validation data, and displays one of them with the `showSbs` function.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 353 + }, + "id": "N6OONK0bL4ev", + "outputId": "99dcb709-c2c3-4407-f0d4-9e9a91cee3bb", + "scrolled": true + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "net.eval()\n", + "for i, validata in enumerate(valiLoader, 0):\n", + " inputs_curr, targets_curr = validata\n", + " inputs.data.copy_(inputs_curr.float())\n", + " targets.data.copy_(targets_curr.float())\n", + " \n", + " outputs = net(inputs)\n", + " outputs_curr = outputs.data.cpu().numpy()\n", + " if i<1: showSbs(targets_curr[0] , outputs_curr[0], title=\"Validation sample %d\"%(i*BATCH_SIZE))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TJtxXgGoJMTV" + }, + "source": [ + "Visually, there should at least be a rough resemblance here between input out network output. We'll save the more detailed evaluation for the test data, though." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4cIO36A_FhdE" + }, + "source": [ + "## Test evaluation\n", + "\n", + "Now let's look at actual test samples: In this case we'll use new airfoil shapes as out-of-distribution (OOD) data. These are shapes that the network never saw in any training samples, and hence it tells us a bit about how well the NN generalizes to unseen inputs (the validation data wouldn't suffice to draw conclusions about generalization).\n", + "\n", + "We'll use the same visualization as before, and as indicated by the Bernoulli equation, especially the _pressure_ in the first column is a challenging quantity for the network. Due to it's cubic scaling w.r.t. the input freestream velocity and localized peaks, it is the toughest quantity to infer for the network.\n", + "\n", + "The cell below first downloads a smaller archive with these test data samples, and then runs them through the network. The evaluation loop also computes the accumulated L1 error such that we can quantify how well the network does on the test samples." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "dD5O58L9HLE-", + "outputId": "89c56574-3010-4069-9664-a59894f5dec1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading test data, this should be fast...\n", + "Loaded 10/10 test samples\n", + "\n", + "\n", + "Average test error: 0.028802116494625808\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" 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QiA3iIUuNIu4kBvCOuZ8yx4SQa1pw9wo9WdJEGnkVY9YqmVcOQbjyxIABmknBMAxVO0ONJEYI+uy02HnfqD9BmBi4crVCG6OfGaEL6eX30w46vQhBxIQQjGro9wxdg3oKQwjBGBp2aBUBkJ7t91QLRsHA/MZQCLowZJ+P0DV+KGNAytq1ahAiBmOpBX3giEofQrANeunukXobXZOg4ycGPP7ylImwFVLg+xWkBv5Oj9nEEkR5LJcQ5KoHMaRNBnWakK+BK69rDKFKHyIQY5viQ41xqEqwFwpBCLLgfQlBH+fFTufASLouM8AYMQci6KVMJNL2IgRbIgM5+XWRgy5iMIZvgW868FWCLkKQSwY2qUI5gzbQTQ42IQYHjausFGxb5k7V85wdyPr6UzVXCYjjXt6h+AJONUj5GMSODUXfbI6CEGQWpkEIctWBtAwTPp6zwqCPf0CCJExmghgrzx59Yx8i0HdpXOgeweiFAWKQQiPCYQeXS72KIYQgaEId62eLVGEfXeRgE2JwGHPtABhXmxRsAzkqQWjg7yIDOQ0o2r+77+JeucTAISWRhfwK+pIH33SQe21yHMy/PShkuph6FUJEJUgSgr5koE8ZhjoOTqgWNDCg78pSCXrm20UIxlqREItFkCIGMbVgE/gqQTQdh9P5l23qaJjjKwCkycEUxOAwwdtpuyNga6QgxbK77HMpU0zNpuNpugjBUDuc9AFwUBQ3G4TGPkcMUmrBNlch9JVopxzLx9otsTcyli1my2GhytvWqL3viA+gzZEq75oxsKnZYCR1oBcBziyCz9ekWUH+NF3EwN0z51FltQiGOq7KQ9735l95Dmifi6XJgX9NaoAHwr9NSjW4UsRgVgqaHrzh83FpPupvkCGE+ypBarlPl+xm0jTvKWfa8hqfICiPBNTqQE0WYrP+FMMeE36j62s26L11QcrBcYha0F8mqcsSUwkSvgfmwp6/jJ9ejgApB0IxuuzzioQpFIIQxjBdxdI0TICCBPjkIEQM/DI2RZ/+QYyaZoF+hGBqJ8NQfyfRRQ76EoOjw1UnBVOiizT0IQRtlp2uoaHzirjFiOtZvk2DODGYUgmQpR2y6mAMQtAiA/vUE+QQgggRyFExWoO5yytmdM4hBiOaEVrl62FTm4IQhPIck5RyqP2Kz1V7Fa+45VAoJjyx1Qh990Jo+DuFzicIQQ4ZGNvBcIgC0Heys0/dxObgefUB0FYLUiaEIUiZDmKEINyomjWvTxnJu94NvIRmpW4TgTAx8NWCqRpGjkqwSWecVAViCKgFLRNC354l8BBVfgMIQV9zRnQjJN8bDRjWc/clCNvslzbeHTFyPHFNV72LnXdkQa4Q9VUD33fANyPI8uVwKz9NTCXwCUFMHcgxIwyBz1kl+ioAofRXwowwmw/yMIXfVCpaYWyZjzwGL20MrqL6kppmaigHIXPCkEBEfdSEvo1oJ4RgytbeN8ZAiBDkkIGc8ouK1ApnHFMNdmVGGEslGCHGQfB1R67rqm85ZgOZBzO1yIFPDICwGSHVp4XO+f0VRz63+650ulieuYjV7FCzjakGfZr40Qz+KeypCdDH5KQgVy0YGgzEX3FgPhswNwlBs/GE7XV+vrlws3uZtyMHcsYvJ8NjmBHyVw+w+Dwszy5CMEgd8DHxSoR2TII0IaDQ+RBis/3AqoMgOfBHhy4nwhEYdSfB2BIh8PPLJQSx+pZbfULptMjXkYMuYoDqfLoPi5s9wyqBuyZGCFJkYNOW6F8vnyrmO5CrAvRVF46DMPCsFEh0OR0OQajSxzx6c5x3/M9djSpqBm5I/xQkBq58McfDsRwOc671VYIpJNsoor1A84aTrEKILUEMEYKc1Qipc5FVBo2ZfxUf16tMXm+fjl2AjSX7LoxJCHJ8EsYgAzm+NNozHwCAttcp13dVP59ctmj6Nv8OCkDZKkeax/kqQcp/oEsxQOB8H+SoASFyEEuXSwyOFoyZFKQg1YIxTAghlcB9lo0oZadzx6trO8oUmAAGz0vVwBEDUNyM0KUWxEwP6WVcYZUghxCMTQZa+Q2ZBozVo/iDvn2xUXWgDzkAwr1mw9GEm6pBiBhU98K0A37KwG0RHcR7lKtP1MMcQhC2FIXrpfsV5MBV90PNa7RQCZyDYlM1YCiihuOhf5wIULbBlnbGr0GVgimVTHlfvx/qIgQ5/VaqP0vxWP/80EE/lxgctVpw1c0HbafqbrVgk7ChvkoQIgR+Y2qbFOr0XQg1GgoclxVffk6ZEVz+KvC5C7np9oIQOIQeMFctyHk5yhvsw6ONlyaTGPjHYr1pSC0IDf4+MchhzbnMWraRhkTmpRuZEPTeDwHtnyhU39piTztNqGZQx3mWedk+S6oGIWLQhVQNdf1QyGzAXhr32ZUT3jH/cw5Skxp53icHOapBFzGYsX+YePVBnBiMrRYA4f4p1tjqc/VnmaYLqUZD3jFXBmdKaPgXRGb+myKUZdQ00IMQ9CUD2e4BGcQgG32uE/dsEYI+xCB1zmd4Lp0Y9LNCGluWXKXNaTgjjBDBQX0iMgB0E4IuMuD/An2X4oZWE8GSASIOEgPnX6AIKAO3yy2B6x/8Qb5LLfA/97mnQ2iQB8JVV+afGvhDaoBfxly14LDBwxvElrEFR8N4vySJQQqxNb+hfKVKIE/LxpZi3y5tF0quK3Os0eQQg6rMJJWD8eIWuE4tZjbY1KkreM+O86wBCnp5pXuDhloQ6E16+R40XkhgwE8RhND3GGKSjxzUfWKQMiOMgKRK0EobODjkmoHoQwgocjx0PgV5rTT/SdXAEYNgmZGvIIRUAgdGd3+1qU9ByDTgUKmagX5tqCKwyYB/0GRBzkD3HFtyNJQVK2xGcGnGjCUO1GYDSQhSSoF/z5ZpNzB+xBoNe+ckMajyR7Oj830Guph2qGyp9ClCsIk60HdCn00McswIvewr8nNAJZDHk8pBxhNrnU0IosRgLORk5VX2voRgFDKQaPRDCEFYMUu/jJBS0Gi3TBUxQEAtkOX1nQ0dQv4EoX4q1l/FyMCmwlDKfyBl0kz1U12k4cqoBVfb0TBdM2NmhC6EwobGwoi6AbiLEEgykKMWSIVAVtwm8Yl/BuoOpmP8y0Y8PHFbJYhhiDqw6crBocSgzgCbOxz6KoH8QbvIQMruw+wRB9shJAhBkAn38S3oi8QGIK0BfioykHgmWf82JQT9NgILKwW+auATA3fHmAmhyjNyxxAh8M8DzT5Mfm+XPh++KgA0zQQxYpCTbgZmpQCoZ/wOKf+CWBqgMqMm4Xvzxt59jBiEmHiVd/U8Xrm8huE/g6LmMkTZgFqsGJjMt0AiphL0JQRjhhEYQgw2XqLoXUuSALjzLZXA3jxkagiVvSH3WIIglQOXLjDoJ9WCVIOwx9kfPQRygh/lEoLeZKBHp5isfx0Df4wM+D9XjEwXBNF3tZWC6rMlBqjIgklfMtm8CeTNevyl0m7yEoPss2JkwH+KPmOP7x8AhM0Gsh/0yUCq7Alr3yAcyLgaAF91pcCgixjU6fxKGQgpOkBN9dm3ZOCy3/SJAhCvfK6S+z4FPsOWxECi7v/DakGVDuaBUw0vdK4uU1MlyCUEU5OBkHQ71MfAXIwG+8omDEFnCgqoBR4ZiJkcvCI1nqG61uYVMiuEpCVZrk16w9ClEZUghxBkk4GBZU75EYQIQdexhvtIox3Ey+eKXhOEJiGQxIDtjZ1akDP4+XsdxMwG/jlZNln61mSm49XLVU4OPkEIzfpDg/uQwb+P+eFocCCMZnKfArnTGOD3f20zwpi+BA7+oB+T6TR7TN7LU6GWBeUYkTIbhP4my420ahA7nm1S70kINiUCuZ7fbqBpkYOGXlkXqHPwz7HFENUqQcvBwiME9nzT9yB8A+mCVj29nPn7qkGgAnWFNB4S8jiYPkYIhpKBkTu+TQhBiAw0yELqvsSNaKiSHPjEwLRZuSLB5RF+Ha6PMf0PVffoIgSpiUsqvHvjuSLpKTCxkeQgNPD7poQuM0Lfgf+oTBCMkZxupsckpCD0Ozb2HI8Qg1B6ZdlCn9cpG43fqKKMXNyj0djEM7nzrkzKOxab9MXP1WoBaJgJQc58ulSCGCHYlAz03n0xMWAHVYMuYhDqpWLXpyAJglIN5lcvV1TN9OGMqk+OINTkwCVRtZyYIAetjZP6Drqh5IGKvhEhGJEINHaWFse7nAe7636bDHT5zBSACVpkB35TjQLEADD+BTBBiswKI2r5FTSCrHmTFHcsRQh8MpCawMh08n3EiAKL6uauTZGALmIwwwfPSkGo/+oiBl1qgYwQ5g+yDj6bZtSNqvQaWSnIQLORxn88ZaOTOZKgUJODLmKwTQwlBDlkYMgWzC1Ug2P7VFA1iBCDEBoqQp+XLwmBOEbymO9r0J0pAEMOWrEFlK05srK4MqOHGtChAADSz6CdNEUI4kGLxungNllg4fsQdBECnwx0+RgwCLCkvYBr51wZCBqbnzmn5o75cNNq01QJ5OfY5AWo31nCT1Q8Q9MPwIeiZlOU5EBWyxTvDh2TbyGXq6ff3BHgqpMCoD0BAvoSA5EWMZbLKCNVKaYMVOSAgTU3iUBoqZAE23RGUTbkJBSR0CcGrjyh430RC9tvPqcr3hBCMAoJiKGDHHQRg6RakGNCiJkPrMmgIgQNP4OOHy7IalVTNXCesS69e75kHt3INinYdDFCEF6OmF+eMVdTpkwEOYQgRga6/QtMn+RMBxputUGd1vVZlRkB1BjAQ9AIkwA5gYmpme56dy1EXq37sHvecH2VZKGLHISaWGgQ7+LgRz/wx8DA7Ggo4Pd50s+gOWC2tyPtFQjE/WVqsG+XvyQEUjUoNderF8RF8npXkeXeKMzcIAfOBFDaawupAqPu5wvRKvy4Bbloz3La53yVICab9okfPxki5KClGuQQA2T4HCDgKCjNBj4hkGSAPNWgegY5qEeEWq2NAlCVX7U7ixAh2MB00FIJ+hKCjvuOSQAcgitiMq+NEYKoghAgCVU5qv7JKAQeJ6hQWjMCo45NQIEhUPYxkjxIMhAjBL6i6coVQtO0EE4jyUKKHOQQgy61YAZmpSA0s/f7tZBqIIlBa1bNMJH/xCYjAKplP6F7+uzbEQJDCrg2IVQNkJNSm7EZEtxKJEkOjNxoiIFGTQQgylBQ/Vk2Ikcqisz3695fTCXIIQQ5seOnQHS1AZAkB1nEwD+fg8aA7xGChVAJSCoGgQeoyqcBFLYcAXfVEDGQNTej82gpAtHVBPmEIJcMTEEC+qBLJXDHfELQIAsRguCvJHXDL6He8MiZDxpBx+AmHVS1y9ILr95cjlhPXIITFa6vSZk3/d8iOBfldlsnaioJNcmAPdZWQFMKQaMMPZvflYA/49xjTGs+sH9lRQ2pBjFiUB8L71Pekq787x7zdmSg5FodKLU5t9Z1+GEAYI9dU+VEZMqjLAmQ5KBqLpYYsH0eV05JCAoKNx7uanEBhFQC+bmLEHSRgSnad3S1gUOAHDSuCfReFTGwUxfzKmvdqfoelkYqVaBFCBwZ8M0IrTK79yhrfogcBNSBFDLX4zaIQih5LiEIdF6DicCGimkuUZWDfIMcU5s4O4IgiYB/jQO71VFsyb9TN7VVDe3zuYkMCYdDAK2ohs504E9U3PcYIZBkIORT0PX7lGgSHiUJSkWMmuQgRQxkGtdlpcjATBTQr83vENsxH9i/MXLgmxMkMWDIgZgaaoFGc0dFFvfwZTmfELjPjgyUViGI+RS4vQjc6jRiQkE1OSjgGn9NDKCBQjWVAakWOGib1yYIqQSm3PaY52zlX1Nfu13IwSgcq8D+9chBSzUIEYMcyJ6KEoRgUdTpfafDqhzus3UeVJYQQANatYgBue64MiO0g+ImnQ1Dx1MqQSutSxfOLznQTNi/pUwHMZXAHGvW+5hC4AiBJANmMK/beJ0pgx2JB4GZUGpTB7RdXqo1sFAM1oASfZYiYNUYfO1foRK4f2sdVw00c4sIVN97dBsN95wAQfDJQZU3NYkBkEcEZLoZ2L3ElonpIhoSVyzboVEp3TGPHOQSgxJCzkfT2ZDFX9nAzD9DCFYVMeAqDdvPrnE0HAeFmkFkBnFGTQ6gjGpgHrImBsRtfwKfjfuw85N/UWsAACAASURBVMvAOzXZ1x1b83yzA4wTgq5d5XYFX76mUIUB7OAv0sSIQUAtEJVL5BdQARwhWBT1dyWIgktXFdaxUF2TAS0ZTZgYNIhAUn2IQAdYbINxNvNhXR+LqQOtvmvPJzghs0HLbyZCCBwZkN9degenWjpCQAWBNAw5gIYmQxAKArSLb+CUThhiYAhFUxWoJyZCydS1OlBaMiCJgO9TUJexXU98B0MZcA2iCTiCIMmBIwZu4JcCW8p/QE5+rrwyINFgWfuNaSMaeg1TkgRfPXAViQRbtVdVxAConXfc8sRKLTBJW2iaDhhrXROCihhoNuQArhGyvZarcqjqnmQID1FFDgBTeCMrutZjpUZU/UNLenMmBF85CCG2a6IhHL5EmkcIQm12iF9BKkxr5IKsHiOqIoixtiIHooYEiYG9X6cDohz4F4VVDBJmBPlMgPn9K3OB9BXwiIFNX/kWRILls2+MjoAd6218r8tWqwHuvFdueLdJEIGxY7DI37ZP3jGVoD5fkwCfEBRKt8hA/dfmSW5yYA5oBoiUiV0AhjENGSLA5CY0DIgwxxIaxs/An6hIQiBXQzXCt7OcTHBVnhRqnwH5Tkict8fcd0EOpGrgrtD2nDQjzON+Lrj7BxsAIvoSAD8EM4/8cWb+O975jwHwfwC436b5TmZ+QyrPrZgPHEK2uoZUGFENHDGQTnuVGQFelEHBpKt+mj3zAQOXpSECK0sI1qyt+YGNYiAyMoSAoJhQ2L9MdhWBIjAzWAGFm5FqglLWpwC1WuBIgPvcZvvhsTKXcfvqgSQEfbaY7YtQHp1EIdVAAg8cJAgeOah9DdrEwPR4iRcsoxsWRZMQFFYtWBQImg7k82hxP6sgYe3UAEsMSnG9e4auqFWV1CUIQOgdsudbkEEIKjLgDcjbCsA21n1idTlFCAqlG2RA2e9A8ycxAzOhUGzUAlsJlf3LIGitsUbRUkQZzYG9bExUmoSgjJAB6QDd4G6RZiSrZ3MTt7qPcARBkgNygls1+Nerv6r+l5vvZlYFdgMiKgD8KIAvAvAogLcQ0euZ+Z0i2fcA+Flm/gdE9PEA3gDgJal8J4to6NsF/XafJAgVOajZqlQM3DJFMMCwaoEgEbJvqBsko7QqwWVpyMCKNVZaowSjZI0SGhpc/TNlIvOPCQTCghUKUtAg808TFkSWodSOSKVjJapWC2T7jX3ugjQdSJUgZE91n7s2ivFeexB9qUNIfs1GR0/XIghusHPHxCyNUW9HTBADtjazu0bJpC+BIwGFJQLOjEBCOZDlLYAqdLG7x7o0ysACNTHQrtBlfc+AL0HyPTTSCJIgCYHVoIOEIEEG+uyO2Bsxp9KRb0HUdiaUJgOfEBTKkIJC6eo4KW71UazJEANNWJcmr1Vp6vcSGpoVFoqxtgOprFuOHLifKkYI5Gqoys/J5VGRgu56IYWnarWEmHS5nMzxmhxI1UBGWK0mWdT8GeuWtrn/QM61B00+xnc0/AwA72Hm9wIAEb0OwKsASFLAAO61n+8D8IGuTLemFKR8yPzGFyIHiuqQwGR1BIBq71jUDLfKC6Yir23jW2mu/l1qjQsuseISJTTW0ChRYo01mOofj1jB/VegQAmFghUWrLAgBSbVaElutkrKmBuIGdouYSTU/gXVO+hg2Y2BPJ7MnKduQtBlPsgpB9CXyGyuRgBoD5KKmgTB/mWb1kzirOZkSRnZQZR8xw3fj8ARgurzwtxPqaZaUA3CGqTJkABXfxwZ0MpWTF2TAHltzrP650K+BNV5tAhBUB0IkYGeZoPo6pFYuTZAmNx2X9cIBS7JgSUESumaGBQaSjFI1coCUCsFrM1EgAigUlXnjDOi6Zka9RA1CdBMDT8C0yd1r4ZyJKAiBRnNqbmZp7mg5CZBqBWEmhy4/oi5JgbOx8DH1ArBQRMAHxWr6oWHieit4vtrmPk14vsLALxffH8UwGd6ebwawK8Q0bcAuAvAF3bddMIwx+E9DRppvO8aTYKgZKQguB0Fa9Wgsuc7gsBsZH6q1xKz/WfYuFEJHCG45DUusMIlViipRIkV1rS21xn9AQQseAFCgSUvUWKBAgUYhVUqYDt9W1RbxpK58kSuGHhAcS4yK77fJn2VQPoRuHcrO9FQhwrkDdjhvSma6FPdnfw41Bm3ehdeI3PdcXPm4v5vz0p53187XDkUUpMQLJeGDCyKWilwEAoBa7PU0FYhm6cdfZ0tyd0nIg6EnqtlOpDP7KsEHYTAVweipoVM5KTvRRwSGBLoq/YPqFUCZy5w/wplVAJVaKjCEoXCEAMqJIkypKBck722qMpVssKCGUpbHwZ7f2MKqP0IHBFYe4TAXw3lyIB0KakdDtPvgBwDRm1erSOtOoJRkwOpHMhAcQptYlCpBdzuk3J+h65jR43+pOAJZv70De/61QD+ETP/r0T02QB+iog+gTnecicOc5z3EnTIvwAwe5WjTQ5IXOeIATs1gdsDVmnVgVIDKzaE4JxXuIMLXNAFVnSBFS7A0CixghY9tkKBgpYosECJUxRYYslLMJbQYCzZTjctMSAmKLueWbvBD8K2CDT8CtLvz5VBzu7T2ymT7ZCUkMblZ/e9D3JMAf7sqAubrM6JXUtuFqxheztYdsSghVOXrLFJNd9JY8OjhUcIFouaJCiPFACGFGgNqykbGrK2BVGSDChUka1iDyUcYXwnw4YvQchsECMEAXUgSBoS7zYXoYFim8RBorXKRk44LKE2JgOuCMFioaGWGqqAJQao5UdN0GuA1gS1kioCodQaq1Kh8NqahlMHqGUyqJyddW06cGTArUIw+TcDqkUjFKIqkH1GF5/AdEKSIEhy0DQrtE0fm8DPJ5bv0ZsOpgle9BiAF4nvL7THJP4rAF9iisBvIqIzAA8DeDyW6VYdDWOIDlIVGeBqmR5XkYK4qd6SMSVocqYGe9z2lSUDF1rjUmvc5kvcwTnO6TYu6A4ucRtrvoDGCiWvzK1Zg0iBUGBBpyhoiRWdYolTMK6BoeGcEJULXMIKSjMKIuNLZtUCs1yJspWBWP8YW4IozQa+U6FPDkL5xBCqw/5Mw0dfcpAuQL/kDNQvrxRRJu3SFFIuDRsCsdYmkEQ1NSLjU1AUwMnCEIKTpTEdnCytgmCbjCMGjhCUpTm2bkSrsDKR6xBS8gAahKBxzDcV9CQEUh0IkYHmqoOePa9q/tJRwtaR7ZROjc3ARGioBEQwJgNBCIoFQ50w1BKghbCrM0OtAbVilBfwSAFhrRUudSHaCKqZvlMIVtpUuwurDqy1UQdKjwzI5dFZIYtBKO25xk6HMIqYJAiKm+TAmRXq/lQ4F6KpFhSJH7KvWSeFgyYAEfTd6jwDbwHwciJ6BIYM/CUAX+OleR+A/wzAPyKi/xjAGYAPpzKdLk7BCHn4251qG3icgCr0CzUSGHJQkBl8CbZB2oZ3qbVVCM5xS93AOW7igm9ipS0p4DW0tqTAziiVWmJBJyjUKRZ0hpKuQZOGZu1cEO3zGoWgIDZx0K35xHUKjhDkqgT1O6gfz0GaA2JwaWTaSkpNDNlynuP7PjTLlTYPOaNOFsaUrN0qBHd/qb0unDeKzUARsBRP7EwD1oeAT5bAcmkIQbGoFQOnFrh9C7QGyjVoXQLrtSEIlyuwWtkVDatAOSVZiD1oXfbYioMWIfAcClvqgPhckwXqfq8e6hUgIV24/UwhshAbX5zAE0NFuIDBdm3lkQNnMigWjOKUoc4AdUqghfkH2He9Zuhzoyw45UFrwqJUKNYaBWkoKurljJA+TcBlaQiBWxq90vWKg9IjAoMJATePV0sIrUO2JAe1Rate4RUiBpUI0f9VR9+/wxGO/2GMHLyImddE9M0A3ggzZ/4JZn4HEX0/gLcy8+sBfBuA1xLRfwfz830Dd9ifdq4U9LFpu6h/zuHckQNFXPWvS2WXHJLzPzBvYqUZ53qNW5YQ3MJTONfP4KJ8FuvyDkp9gVJfQrM1CNseslBnuHb6MC5Xt3BS3AUuSqxwB0wPGPdDVlAgFKxteFOu1AJ/zJQhjLueOhaXoNmYwiqBTwh8MpBaQigJQ4ggNE3wA1WDHoNPFwHovEbVf5kZpO0ac2WnaaVuKgXWTFARgtNT8NkpcHpWOxyuVsCd24BmqLe/C3j4AeDxjwAP3VsTCy2m4m7lgSKjfrmVDQCcUyprqg28KSdETyWIPr8gBL46ECIDfQlB0unQJwoBkgCM3kdG4UcMlSsSnDMhKYZaWoXgDCiuEehaATorKlIADfBlCTrRoNvaPleJk5JQlgonpcJlWXuvrphwoQlrazpYswlkdFma/mjNtelAszMh2NVPXJOKLpSipVZB1gQhcIqmto7ajhxoYVbQiBMDuPO2/0oRsdBgX/c/eThK/wPGFOYD2JgDb/COfa/4/E4An9snz8lJQV/7dei3lw2axTHNBHYVGYCLNVwqwlLXeRlCUOIOVnhWPY0b/Dhul0/iYvUMLtfP4p5rL8SnqFeBwVhDVyqEBuMEBe4tTnGpNZakcKIUNBhnhcJZQVgqo0wsFGFBpuG//+YKLEKZalFu+Tb8N+NXfPeVqkYlOzPvWsQJQWjXOAnXIVTfPYLgk4NJVQMkBqjA8VDaxmBT1n4dpAAs2PgFwlQQOhGzb7fUcLkErp0B166B/vRx6H/9e/jwWxSUdTor1wq37pyAmfD2Jx/Ec0//FI9fnODh0yewVGxni460NZ/8pCjx4keewvVPvg46WxhzRfV8YTUgCU8lyCIEATIgA4ulQk8TceudV8qMDl8TjKmb+3hWdZPqgH+u69rQ8UL66ViVQBXGZFBcI6i7FqC7F6BrS+CkqExDdFmCL9ag0zVoscb6FvDEU3fhAzfvxkWpcK4Vbq0L3CkJt0rCeUmVQyEBOFEAFqZ/akc5NFL+nbXG7bLEinXLqVCqBUq8EXITIK7TqWrQN4Sg+bk2K2ihGoSIgQvCljuqhwbwmGnBT3rwg38KE5CCKTBNnALKk7dTyCETkhgUTCAbS4BBWClgaWtYyYxLXeIm3cQNfhw31x/Cg8tHcH/x6bigO/h49WJ89SNdUyU/Yk7YPnx7rcBY4qwwz7hUqMwZz1yi8t6NwZdUVTWg2/PynFAJGnmgSQj8HeNaTyZ3c0NbAZCbVrn7hoiBvKb1XEgTg6FEIGYPb6SR5VgBamkCVBXQ4JMStNZ1oKOzE+Ajz0D/6ttx+egKj77nPrz23Y/gdU//ZqL0+bibHsIXv+fj8MrfO8e9yxXOitJI18S4fnKJF7z8BhYPKNBSmRlqw3Em4EuQgwQhMOvv7XsKZecRBEkeKoncIwPJ/SwyfRZiJoQYSWA4z3jjx4OqPtamOg7se+yWHrqyqiVAp8oQgvvOQPdcA5YLo+ysS6AswY89jfM/WeHGh87woafvwe8+dS/++FZ6f1MCcGaT3FUdaacqGbixUrhxqbDSzTDHpWbcKtcouRlPpfIbqNSBAEGwxk5HDhjO1FCrBjFi0DQjNv0KTF9T/5XHgfYgP4QQHAVRmMbRcBJszXyQ+l1DBMCXyWPpq7jkMEFFFrbCrrnAsjRdx0oDN3GOp+gDeICfj7uXD+FV938cPvOhS5fLkEcK4vpC4/M/6jJ47vefOQHDzBYUGVLj/AwaA1dABQCEfwE134mvElT5CELg7xYnUe/1QM3AvEIByCEGXQjdfmMyECACbrBrHJNmjxWhWDEABhYl6HoJpQjr33of+FLjsd+9Cz/wOy/GG++8CcYn5z3dD5eJm/wkfuHGb+IXbrTPPQ8vxV9+9OV4xd2WMCxKLFWJ0+Ua9997B3c9ZwU6gYm9L0bM2JJDqR7I5/cJQehdVmmb2+lVkATBJweN6yd0IARc+2/XbTOYmXMMszrJjp2ViufiDgAwCoZdpUInCnTXCej+u4EH7gHuumbMSb/9H8BP3sGN31nhje98Kf6/Z8fvPgsCHjgBHjhpU6JLDfzpbYWL0kVhrX0PSuZKWXDqQvVoMM7OPjlQLEwKVjwgq16QXdVl2rlRC4jaoT18OIIgzx/DmD4KZlIQrwx9SIDvbNhcVgQ0htMCWGnCggowCLdLBSLgvCxxHac4xd34ho9+CR65awUgPHBPiU+4L3zPZ1cFnlkrFAS7jhrVZwcXFCX9PppmA0cICsVBKbteq+yWT3IjeJScNbgyhLaw7ou+fgJdyoAvg8tZMWvTybm/DqpgLFYap6RBxSXWH/wwvv2fvAy/cvNduM1/BOCPBj7dcPwp3osffOy9reMP4Pn4gnsfwRc99wIPnl7irFjjpCixKDTuOrvEPQ+c2+Vz1FpC5xAjTP57cqhjQRDI5hcjCH6dcD4HfjhcIHwsBqcW+OpAH7XApdVsA4jZQGLMZMmB8QdYiGhzdFIA106Ah+4FP/+5wPIE9P7H8BuvvYbffPI5OO9YRDIVThTwMXeHycLTl4xnLnQVut3BhXD3YYITcaUSOpOCVA1K+4adCuB8ooyTpslHqgSyD48pmzEchRrQgQlWH0yCra4+GEIGpOwtHeZCm5wAwBkD1xcllkrjvFRYKsNtv/6RU1xfvAxAwBN8x7hnWeKeZbineWZVVA3OD1YENFUCB6kkEBlCsLDEwIGFoF/PqpzHc/PXixGDkPPhRvD6Lr8vi3nLxwY6XdpgM/azLhVKrezudsDJ7RKXt9Z4/2/fj2/43afxFP6fkR5kXDyFD+AXbnygpS4UtMRnLz4NX/2SNR48ucRZUeLE1v2zkxXOTldVMB4A1eAu4ciAmzXLNFW7Ku3x0ikD3T4iXU6EIWIQKp9Dyn+gSy1wEVHNLNrU4FIrs+rARigsVwrFpa5NIWdL8MMPgu+9D+pNb8Pf+65TfOTy7vRD7QgnCvioM8JHnTVNGAxjsvzQnbIV66AUP1BrH4MAMVDcXo4oFQFJAvoSguq+EeXhKMCYlQIJvzG3Heq6yYDvZFeQrnwXCtJVVDIXrvSedYGVVvgDdQ1f+wigaGIdcyLcFyELRaSDbHhWoyYT8n1ptnIqlG2MLsZ6LQf7pgSJlCmhGRSlO6qlSdw+lKMOuL8xdUBbr3CtCet1gXWpcLFa4Hy9wPnarCd/7Xvuwa+dvxXlHpLFLpS8wm+s3ozf+IP2uVde+2x89nOA559d4p7lCqeFxkJp00aIcbJYV2TBJ4FymV5oK+EqnSMbvp2emmmC15I554gIESopn+1OpEDbtyClFtQkAJVaoOGim5r6brxMbeRJUlhbR1QihjovUNxao7i5gtIMft7z8OjXvQE//QcvwOoAuw8CcP8JcP9J29/hqUvgxqWu/BZiYEblzA2Y9+t8pApPHZCEQKKP42Hq2MFjJgWbkwF3TUgZKFSTDCwKjeWixMnpGienJZbXNe574g7ulB89osfA/qC0jbQEINc+LolxfVGioHrGbyK3MRZKW/mWUGqFwna5rq66jaZ8U4JvRnBpUz4GkpzI61rNIlcdAEYhBHdWCzxzeYIP3DnFY+cL/OqdNzciWB4L3njnTXjj+9rHFQp8NH8svuQ5z8Mn3X+JU6WxoLp9LV2bsisoXBsjMjE4/E2E6i2HzW+wKEy4YAf3y1craCoiYUkBNRWCEDFwSJkRahLaJgYlHOG1FMMRg7L2Lyg11T4Sag11/9PAeo1//O4XxHa1PmjE/BYYJp7CjUvGsytLGlH/ZkuPEPhOhqnB3h/n+5KBWts8QMyOhuOoAy6dJATOUztGCE6vr3H6oMbJx5zhrk99Keg7bqEVMOCIsWLCM6sFVOCn/YSHP4JVqbAuVTUMur0BWoO3szWK67Nn/glUV2c4peUQgub1tf+AUUPqY6UmrLTCRVng1nqBJy8L/P0P/dZREoIUNEo8Ru/G//7Eu4En2ud/8JHPxIMnKywV41qxRlEYYrAodEOJc20PsGqA3USosH+BeuAHUPk51OTA3ZFbg391xhIDqRYMJQYaThljaDJ/SyIUbLZCXpeMdalw5/wEixsl1PsZP/X334E1Xy1HOYIxRzx8Rnj4rP3kbiWHIwO+X4c75+DMAk01Ma8cfY4fBLYVmGNDbCFOQfN7yokQ6PYdkFK466QKpVEU2oQpLeysZc3gd7wPX/xnb+LXfuPFWF0RYnCqNO4VcvFSldU7crMhbZuW2Yo6/F6cUiCxKSGoM2ofGkoI2kvtJBkw5EAzNZ5lqTQeOinxfS/6NLzo+gW+8T/8AS745jjPtue4j56Hz7vrpfi6lzyLh67fwdnJ2tsM6P2mDS10a9MqM8i7z03Tg4vhANSOhq3liJ7PQI7DoSQGQLfjYV1Ha9XLkQrn5MhwfgYmoqCiwipfJ1hrwqVWePT2Gf7kJuPdt57F5zx0L5YT7M2wjygIePCE8fxrKyiYuCuXWuFCG8ftS11vy1yv/Ioj5nzorrkavbJ53qlX4oyFTlJARNdhQiV+DDP/NSJ6OYD/iJn/r65r+6gD5l7dzoRy61MZv1x2UvqCUX740vy7A3zOyx7D+Z0FLi4X0Frhcl1UM0nX8d1aLaGZ8KfnZ12PtRd4ZlVgQcCJna0tFeNUaZwoDWbCWpO1pRbQ0JVznQmYQi3SGhvwY34FfZFSGfoSAnmdSyu950OKB2AcqJZK43pR4jmnpkx3Ldb4hU95Ie45u8DZ6QrLpTGxlKXC5eUCt85P8Ec37sGbnjzD05eMX3p2P50RfXwUHsHffsWDeOS+G3jo/lu4/tDKDtIlWP9B7Yy5br4jIrNHhFvFIIlAOPZweEiQUQ+jJCGClh9BhynBfQeaPi2oVDBjbjNL8sygprzhSAMoWWGlCc+uF3h2rfDAKeERfTf+5GYJZrNNBqEOVlaQ6T8KAq4vqBGLYN9REPCi6yXOlDE3nlpfE/ceSyYzkRJ1Y0EMJqrIQig2SYwg+C2/DxnoGwBvxmbIUQp+EsDbAHy2/f4YgJ8DkCQFuQpBdV4c85fPKUEE3DI9l15eZzzMgfUdgrZ0trxQ1brbQtmgNUoDWlURxUpWWCoNzYTnX7tTzaDNvgXmQdbimNkqtZbcnX1fw6QviLGy9vgVu3j79QsxIU3rncgaY6Ab5MyKYazYREpsBQsS77Sw9l6Xn+ngzEZN0ACzaszKKm9zr2mOsdwwhE0JQesaP69EQBy33tr9/stCAzBL+hSZCIMnhTEh6FJhBWCx0FielDg5LXHPA3fwvI9+Bp8NMwB8Pz/X1EU7M5YDaFW+tVv5AOiVwmqlKoc61oSi0ChLhXJthr7Hb9yNh+6+bcpYaKzXBU7PVtBlPQuX2/ku7y5NkB0F0NItQ5SDNwHqNmh5XgXB4LUy6tka4JX5q8FQCwLLjZRa7xoVKySgMVo3yIKcyvvnxefQltl+3UQgu5hiYB63qRq4Y66NOWdawLVb44tTxYRisrZ0OyNeK1xqwlkB3H+qcF6acMQSrn9jNnk5+/vNlSm7sbkLsuVd25wMhW3zC2Wk/POytuGXXEv4Lr37W5DpS2T8E0U2LgrMsaViLMgM8KeFIQILcvu01ITA9SEl11spky0rwQwcuvFuzXscswc5KjJgZKxdlyILOaTgY5n5q4joqwGAmW8TpYW/NivsNhmYc37a5vdQHmxnBNWa44sC0Bq6NF61vFZYr1V7NkTcYMFEpuG4pXqVXdLmLW2VCDwPo9k3utUBS7jGI6+p89AeOaiX+dnrq3cjOxhudAZ+J+PCPpdsSsQgkNcTOwLgSE59rXyvTXT5FKQCGoWuTRKCQDpfJciFqgihxgmMjdxuoFyZVvyAWKRhZsp2izhHBIKzaCX+EuyPwdXMHKpsDtgAoGpfhufoizq+PgBg1doxy2ztbP+5cMwxWM2cK0LA1bHYDxScxbvt8RrH3HOI2X/Hb+H3FtHeQ1G1/bW8nU8MvGK0VAKgWacdQXCQjoOO1GuYGCeXWlWmxqUyM38FEyFVc9NTP/YcBer26pMBV+bU9XXZTAwC+VO7jd5cfrIPMOfdhnE1UXCxT6QKa96Dnfh4BXHVxJECRq3CuT6yqhqizcfefx8cFRHwcUSk4JKIrsGOYkT0sQAucm/QhxCE0sljcYckI/dW51eqCkfg1qiXpRrNz8MFTZKdkZttVCFC4c12qsHeDeptnaDZkbWesvpE9j4uKmKLEJClAuzuruFkVFcG/55TqQSD0XcL3wCcZzxTrRQQlSA7Aro6tSh0tIOWa/eZ6x6QQCaYjxY2UmG8NkvsYAY6uM8ANNcDqRglqjDLJvOWV5YLfELmx412MFxPfxuEgNdNc4t5PwArt2QvAH9b5IAJoLXfQUAxcPfMDlrEzfrvEwOTV5wcAM0+pIwMUNqbEa81YaWpIg2FnWUDAFk/lsSrb8Fvwz4ZiC3nI4TafztvnxD4qLZQtgTYoXSmFW36BDO2u8lNPVlwpMF/Xm9eVE0GmpOb8EvylyofNQnwcDQ+BQC+D8C/AvAiIvoZmB2XviEn8y5CkE7f9CWIwSkExEb+ZeZG5DWujtcOZ6FZcLMM4fP+en6g7TTTIgbkL+0zaZ0SoBoP1820HRlwZa1DGLfXg7PwuqZK/QjM2EVH4EPW49EcDdFfJQieS6kSCmAbdIeYUSgA0CAmuJgV/np8t14+G7pJDKrZMwCuBhNukANA/MoeQWATOaYCOb24OiAG/Ric0iOUARkGuYKyZdao20tAGWjGHEjfugud5EBz62PVXuxxnxyY/LhRT32C0LhFozyEtW3Pa7Gboby3kdzrMrH7y+E8ZRkk5DPHlu+Rdz4WECi27r+OaBookC236ZNMj1AyVyZReCuQqueEJUJen1jlw3W/Js0MLg8fcqnylYJ7oQeATlLAzL9KRL8N4LNgfu9vZebAYqYmQrI/0GwsIZUgB24rVLfBiRnobZQy5kbH6YhA5XnvyeN+Za/L0zQhKDtLaKdpqgU+MXDP2Q4fXKsGJify3kNILTF/Q1smh96RXI7lyEd81iTuPOLgL1EpKl2MORSD35kQepSN7G/kbPiFAvxd55yTZFM0DwAAIABJREFUalHoYKCeWJS9oONcY+2c+VyF9dW1X0n1BA0JXlJLew+vDlBGQ6mVAtSEQMfJlZT/ecDqgCh8b0FZRs7PO6QawMs6RRAQSCc5kiMEjgzI/oBQ2+mhzE7bbsrgbqm5uR9ADL4q4B/vSwhy1IQQNMwOkTLao7tf5RslJj5rjwD5II8YuOvlMx3GULgFHItSQER/EcC/YeZftt/vJ6IvZ+Zfil/VrAZ9WGFuWkcIDPulKmypoqaNrHKqE/8qNjywtjZD/bZNB1I9qMR7sTqirRo0SVSbINT3hb1Odop+mGO5M5y7pxbXwksrITvYKepw6PedYvmuUwsMwTCDvotf4KejKhBPTQJSqkFMRpe7BNYKgEhkRzj/fEOCl9NUbwTMip0eUgVisGWoVgckBplBKsFAYuCcbKtsPKnaZS0RUhD8osi8mk7DYkYsiIFR36ge9FUtk7uJX7UDY/hRALR1xxBBiC3dC6kKMYUhBUmuzL4HrtBudUY7vf/df0bfRFp9pub1MzkA4E1W9xlZ5gNm/kX3hZmfJqLvA5AgBTViZgNfJfBNBzmobfdcLTHSQKv21WSgJgibOMKErpVEwE8rG0xMNaifKaGyuFmEyCvWVzc2h0HtpBWrl62YBN75MU0HYyG0jt5MW0Kb+xjttAi8W0kIusxVnQgMhMGNgQLEoUE2QuRAwrfhN8rAVX4xZlctGfSIQSxtF/osOayuSZgTalWl+XuFCAKQJrANFcxzsGXUbYW9gc8Nyo4IeL6frTYfd7JtXxc6H1MN5PcQiTDf8wYc0yeYvQwqQS5wqXuf7KknIbh2mCIGQPu5c0q8f73OBjgWpQBhrr+1LZclmJ3XK1vnmbpP1WCAqZLJZfAdubQwmCfXm8L0gav4vuNMyIwg+3BZtpZ/VqIILedMdHcGlRkB+dL7TutuYGe/HLjlftV3gtnYpXrBZN+7dx3V1ytBDvoQg5xBX5oRaufBMDEw5XHlzhwFRbrah8D7XuUTuB49BvU+g39CLXDoUg0cUgShC6HVNlx9R8tu7pzp5GzdLQeMkYGYahB6tJAyINPGCIL87KsEOVXWPbMjBoBZUhnazRIw7yT0nkNLS4FuYtDII6O8R4XDEAqyBve3EtHfBfCj9vs3wcQt6ERswIqtIojBsVU/PzkLrwdXpw+KwaVhMhAkIFAtU174jRm/MCGgOtYkAv53mUcrIlzqBch7CEIg7xuClGFDttgQwhPPfu+pD8Y2HTi1gDVVxABAgxw00iunWLWJAClGa0leAl2z5U5iAMTJAZA1CkYJQeBaXy2YDD2IQXJznkCdC6lqoXR+TAJ3zJkNQqHpjWd+3c7dgOoGO+dv4A9+oZ8pRPb7+hmE0uTcx5gLzMtt2P3J9T1tsqwFiQK6x7MoSZD3u8rgTPPfHiCHFHwLgL8F4J/Z778KQwx6YUPH5QY0DMslahIDAFYtqF++JAPue5ck1ue389WCPsQAaBKk5KqIgBnGD/DkP0PdIOt30rfv30ezQQvWZCDVAhLEgLl+P1pUxIZDoVAMGjH70Z5FdUL4CgR9C4AwMUA7rZzlxwhHSwkYMsDnEoMxG7KHOiKld8vE+0+R0/DSWznQ1bNgOQi21CTIdu63rXYZYzPk2KqB0PkcxaC+Lr/DcmV33NRkHn6HPiEIPhPa1SZ4rEM1uBI4FvMBM98C8J19M86pqMmlM6EBzRIA42HcJgbV9Y3y12TAfK87AKkadKEh+XcE8Wldi5oYyOfwy9uloPjOhS7vFJxaMGRdcDQK4R4If9I5LmQSYBYDu1AH5Hptf4tf/5hUCULR+XrBNyMAaWJQF7hCljlFpEmpBEGk/BS8svRGhloQvXSDwcSPz99yNERdfxpjpP1iCAFXkRAlMQDiM+Fo7IDAsU1WJnShqaLW8QqqZ0X6/fqnuvwLHGLEAB33O2YcTZwCInoFgG8H8BKZnpn/XO5NxphcyM60Dnla28VkZa3NsdTKwycEiKTNGfSdCaFLLTBp6wbmk4oYofHPVcc8QtBFEBpmhAxysM/qQGijJgAttaCuK/WAL49X+QUUgV4qQSjqn0BwpUKCGJj7y/zF51hDCnQ2oQ4oRH6DDoZTqQEbEINet5Gmw8Zxd6xJCHy7eR29T5oLm4NaSilwjokp5DofdhGC7MmBqHONlQiR+7fIgHcgWJc8gh4Tn64kOWAcj1IAs8/BjwH4cWDzfWb7+BP4S5OAWjloEAPPJiYJQui4HFTk+TFnwDnEwD2PRKrPbMjdGWXw13g3zw171jHf0Vj+BFVnJIgBgCg58K+tPsvzAZUgWHdjhCBoMqiPxYiBKXdEjcjoVNqmhMPreVOBu3LSAXEyIL/HIJUCn/DHyEDMH7QLMTIgzw2JRxCD6y/98sdeiY70q7lIWaV8InXMYByRUgBgzcz/YPKSJFATAdTmArm6QFSuNEFoKgOhXfWGVs7QSoT6XJgYuHMSoWt9UMf5EGJLvPqgixBM2bBDM9rGTnweMTDn4wpBO3/XS/ZQCQLoimHQON5BDOqy5d23hcwfJLUccXRsZEZI/yChp40RAqkShKAQnwE144vUzobyWB+kVINY/ALzPf37+qs6qv5TmBGii1sGtuWQOS/HXSVlSnbnD5o4HJlS8C+I6L8F8IsQex4w80cmK1UAIWIAoEEOnHLgwycD5pj3PXMlQuU3VpkK2qsQgkGMPGIAhDuxroaepxBIP4xmgwupLznYhR9BbLCSJoQoMQBa5KATQVNCfc/G96EyuO9I6BMDoNVDDhqwvR60S5U5FGLgI2eGGyIEflpGe9Bx0frgEX5JAmS+/nG/HA65/gYpQhC6LjqwIiRaNfdDiJVVpncYovANXeASW7p5iJgiSNsUyCEFX2//foc4xgBeOn5x6gHNDWa+PVwSA3cMaJKDWL5V4SOEQHM7bR/EAnbETAbyLl31JVSivuaE+l75qkEuGdg2i+8kBkBT2o9tsDRhrP94YCKkB8WU3SfrvtvvfTZ+VwKblD5EBuTxrqA8XRK5TwxC/gXm3i59d5lTZCCWx9C9Axp9p72zTw4khpgMcmIYXEUcjfmAmR/Z5AajOBlGiAHQJgfJfOQYMbEzXZfJwL9/f8kxv1OIjTGxwDD7sLoghK6oe5IYAF7HlKEWxAiBrxKMjWDwniFG6ggZyJ2hHKpaEHrsLkKQujYERwAMGZCreep8NjEhyLxix1IqQeOawDH5uv361mfg33S2e2WJwQGZDzqbJRFdJ6LvIaLX2O8vJ6IvG+PmXQ0yzvqbs373Lwb/fNM7uakShO7XhZwZd3s20N7VMAeh68YYwhlU/euLqX0Jouf896D88/W/eB7tNCFCkFumbHgdRLIOV2H3Ov4F0LcTH/JsU5GlGFKP7dfhGCHoMxCGAoW1VwCJ9FT/68w7ktY/Ro1z6R81Z+wZMriPtvX8ONnMmAg5v89PArgE8Dn2+2MA/vbYBYkN+iliEFpyGPoXuyZECNpLE13eeZ1Is3PoHrxzycFQEuGwj046Qxz52gN/mxiEBik5+MfIgn+tzHuMgS8nyNCYdsfBG35F3mEs7RToyX2ChDbmQ9C+No7UlschYuAP5vJY6J+P0PEYIWgeF58jz+K/h67JlJ82J89cXEViwLrfv10h57f5WGb+QQArAGDm2xhncppEDjFw6UIEIXVediApQjAGcmf1btCP/Yth10L/JmQj2NHkyPwdxMCl6TOwpfKMBi7KcWDsatwTEYMx8ki9wz7vN4rIu+lTp2JkIEQIfJWgz31ijyqJgT9Ib6oWtPPs/6Pm1IMUOehDHCRyCL/C1SIHsUlr12R228hxNLwkomuwhJqIPhZiFUJfuPgCwXMJz/hmwA1nq2cvTbomxmYS/rW5EnrTb0Bup5zucHwfg6FIEYwQNvVfC+W3Cbpi3VfpMmzdsaBGfQeuLpPEpsjdTdD3mel1j5E7lG2bB3IQa6PNdfUi/YB3kgrXK9tw18qivm0upih2pclBypVjVwPRlfAzYMSdnfcMWVsnA/hXAF5ERD8D4HMBfMNYBegKsNPcx6C9vM4hRiZyOo9U2cZAyrFw6C0GdwojEYNJzREi1oCDTwyCcQu8Tab6IMd3YLLBMdFTy466iyBkd+oH2AP3jZERIwQxlSBJ4i15rZcjh4mByadJDoD8Np5L8tv+SZk3sBjLx3PMapSK6noMYBzJ6gMiUgAeAPAVAD4Lpj5+KzM/kXuDvhXQVwtCxABoN4RNls4NUQm64KsFsf0ShpCDTUt4gCvegkjFMXDoIggx1aqTAPTYPdFHUC3IaCiHss55DGzUnmNpg7FIhkESA5nPGCuLmvcZRyHYBXKVQB9dW3AcJmjQZGUXSJICZtZE9DeZ+WcB/PJUhegKsOMTg9A1OfdoH/McFSducqmNlLpmFmOXbAgx2CoZyFAL3DEgzsL7btMt8+w6lo1cZrylvQH2HWMSgrGJVEzqTqkGg+6TWW+HkvtNq9o2BuyjUg/4SJQCi18jom+H2Tr5lju4SUTDlF9BDDFi4CO0k1ksv/T96s+NiIiJa/zohqEy5OywuC0+GVNdYummQN/ZRFQd6CAHuXlnHU8EPMpFcn+DKYnBgXRMKcTqY4oQSJUgpz77JoNg+F6vffu+QqEt3FPYtkPx0KrWpwoNVQt8HANXPiZS8FX27zeJY4yRIxrmhOPN2+Gv+TecJiQj9us0NkHfrZf75t0X21IAQsQuiYBaAKQdD4eQg2QshF30RrNi0EJnTJPtFANAUy0IEQOgrfZtspw47msQLpuPYHAsi7mqbQeMYb5Ou8DkEQ03QWw1Qg45iF0Tu89UiCkWUxKDq4CuFQljDObBPDbwJfCRXIkwRW99IDMVh1yyGnqsHJWg7y8pZ70+MfDz3tSRWOYRwrg7J9o8e6Sd0RMM8IGsPthKRMNwo22/oFi40vgKAmr9i50LIRXwJFXOsbDJ7GEb+W0LSfk9MQhPNYuPrsHv2Hp5dGhc2V44a3UQuglBX4T2AYjGKPC/B+oCob/033XNVBsDparbJlVx0jZyQDiUOAU7jWiYSwyAPs5HcRLQld9UMnqqEW8aqfDQEHW03IAYjBJIBx35jKgQ9MYYxOCAyEUuIQjB70z7bujTld6vpznEAKgH+px/KST7ko5rc6ED/2ZsDmbq9W9X2MuIhn1CmfZBKr5/UKXwQx5n3GPItsTA5rP8fScWG5tKMiMdDo3dn7wucu8Qkel7/2zfh6G98z736gN+q9TjdBGCHNOBVApiRDWHGEwxm98GIZgKs1pgzAd9/u0KW4toGDORxlYipJbM5QQtiqUPli2DEGyKrtUQJk3/pUz7Tgb6oNNT2Q3OHQ1mVLPCLhWCEOSImHrOfSUCA9G5f8HUzsEdZYhFPwQ2VyCnMhdsG2OtRDhE7Nok0Ac7j2gIDCMG1bUbihb7EohHor30sv2Mx0QGJLI6jsiqhNGRMluMePvc0MctHNnAH8LQHf9SKsEYCC5RRLi8fh+WU5Y+RGDfVQKJq0sMjiB4ERF9LjP/JoBfxwYRDSXScbfjxAAYny2nGmbQ12GEe+aoBeHrDrsV9TUd7JwYdKgDsxQ6AjpGsk22/+3tR5DZvPxBP0YMgA5VYcT6c0iE4KpDH8jqg5RS8MMAPg3Am5j5UzFhREOHVFCjschBVwfQh80NsZMPJQYzAsg0J/TOL4EUIdjEbDFYLThCbEIIgvlt0N66CGrs/DY2+cmtLvtGYq+kWnAk5oOVXYb4QiL6Yf8kM//1ITfsWn7dFe2wHSEw456ZP0aMEPRt3H5kw6uMoQ6GvToOOZgPIQj75jcwI4lUvchRCTb5tUODfYoYIJB+UxwDd3RE5VAGyquEFCn4MgBfCOCVAN425k3HjMsy1qx7m/aeq6IWbLriYFDHMdEA3zXbGmOWf2XUgoHP2FUPQoRginbWhxi49AhcM+S+x4arohpMFdGQiL4EwA8BKAD8ODP/nUCa/wLAq20xfpeZvyaVZ5QUWL+B1xHRu5j5dzcpeF+4lzdkM5uh94ohGszjQJxG9hFd0Qhb6Xfcceyb/HoVMYQQDIUCN/LLrX9d6UIbY3aXZTMcQt3ddfveFsYmBURUAPhRAF8E4FEAbyGi1zPzO0WalwP4LgCfy8xPEdFHdeWbU+fuENG/JqLftzf5M0T0PV0XdW44lHHjqYM4DCUEY+BYlhnF0LnpU8/ebledW859r8TsfiwMeFdDCUE4Quo4iD1Gn3qqMv5tgkMgBA5Eh1XeIQhF4E39y8BnAHgPM7+XmS8BvA7Aq7w0fw3AjzLzUwDAzI93ZZpT714LwzRc8KK3A/hLOSXuQu6gOzYx2HXEqBkGQ4jBtjqOXXVSh7KT2jaw72u7U8Tg2Ae4qXC07477BS7KDF70AgDvF98ftcckXgHgFUT0m0T0ZmtuSCInTsF1Zv4tav5S64zrsnbFy/Uv8AfxvqaFviRgK/uFH6lvQR/TSl9TAjC93NinU5pVgh7o8a5yf98+KsEmGLLKYJey+KEPrLL8+0wMc2F8Cnpf9jARvVV8fw0zv6ZnHgsALwfw+QBeCODXiegTmfnp1AVdeMJGMXQRDb8SwAd7FiyJIY6HoUHeEYVNVYCuMWr2J9g9pug0Dr0j3WtMQJ7G9COYCrvwsj+2enwsBGHAuPEEM3964vxjAF4kvr/QHpN4FMC/Z+YVgD8ionfDkIS3xDLNaarfBOAfAvg4InoMwN8A8I0Z1wHIfxFjzMzHMAtsW709Nt+CIYRp09m2kxyHdIYbXZtb7qn2QzhS5HT8KUIQ3ztlM8TqSM7Puw1Z/Gild4FDfr4JNkR6C4CXE9EjRHQCY9Z/vZfml2BUAhDRwzDmhPemMu1UCpj5vQC+kIjugqn/t+3N/ySn1H0wxRbyfe/fmWZWCSbBEDNCMJ9t+RzMZoN8jPyu9lEhyA1WNIVycMgD5VUBY/yxg5nXRPTNAN4IsyTxJ5j5HUT0/QDeysyvt+e+mIjeCaAE8B3M/GQq31SY43thVIIXAPjnAH7Nfv82AG8H8DO5hc/xLajS2r/b7HPHnpj1DVx0LL4FG8clGIkYTI2ZEOwO2yIE/rLEvGvy+5IxyMFMBg4IPE2cAmZ+A4A3eMe+V3xmAP+9/ZeFlFLwUwCeAvAmmGUN3w2z98FfZOb/N7/YBn2IAbA9ctBnDJpVgjjGejduwN1XctCbEFx1AjHi8+cM0lOT65xYBH2qbmpgZ54H/mPCnnZpLaRIwUuZ+RMBgIh+HMa58GOY+XzozfoSA2A6ctD3B5oJwXaxj6rBrBD0xB4RgjG5wtjEIHWfGceCw1kGn9z7wH1g5pKIHt2EEDgMIQbAOORgz8aYo8FUhGlfVIPBZOAqk4gtE4J9wzY2RJpxOJjCp2AqpEjBJxHRDfuZAFyz3wnGVHHv0JsOJQbAbhraNn7MQ/Ur2Ma72aVqMKsDu0UuIZhmj4O4X0FODIKZGMyQOHilgJmLKW+8CTHYJvoOevPuiNNg26rBxmTgKpOJEZ79ENWBEGZiMMPhUCZ9OcGLJsO+E4NDkXt2hV28HzlYj00QZlVgBOzgHXZ1tlP1MLkRC6faQnnG4YAnWn0wBXZKCoD9JQYzIUhjH96PP4j3Dpc81QA2k4utYdezrz6hjGfV4GrjUNSvnZMCoB5g9oEcbDLYbWo6OFS/gn3BXsz096EMu8JIzz5m57lvzWlWDWbsO/aCFDjsmhzsw+z3EDC/pwhmQrBVbINA5wQxGrLx0UwOrh4OZd+GvSIFDts2KYwxyI3hYDirBAeMmRBsFfvWVobuiDiTg6sBBh3MZGoiUjDGAFnnMQVBGPMHukorDkZ7b7OBdcZA7BshGAOSV83N4jgx+xSMiDHNCofC1o4aSvw9hh5w5JnyXvhGHAHG4g65+yAMVQva9zM4hqYxo8aVNx8wCDSym0/XgO6Thm0QgFkl2BCH3gNe9QF8y8+/7yrBWMQAmNWDY8KxRDTcGFMQgxS2/dKvEiGYHIemGlx1MgDMhCCCMYmBQ+hVH1JzmXE448Xk5oNtE4MZ02ArhOtQiMFMCPb+HYzd4/TdSnkKYuCjz09wCM3qqMGHQ2q34lNwjMTgUFjfwWHfzQkTD4azP0EYh9Kh7iuGVqt9bYaHhtl8EMAxEYOrRgh2Upn3zaA6D9Y7w5SEYMq8t6EWTI3ZbDEW6GDGja2uPnAv5ZDJwVQ/7DwTSmBXZoUtE4GDUQm2WM4h7WKfmtIxEAMf+8bXDwWH0sfvZEnioaoGh8L0jhLb7IkOZXDeBfacEEyJvn4FDsdIDBxmgpCPQxk/dhan4NCIwaH8oGNjL+1gY/dEe0ACDkYl2BK2RQi2td/IMRMDh5kgxGF8CnZdijzsNHjRIZgTtkEGDqWy7CWOYDA9GEJwAOXc56Z0FYiBg19VZpKwpxOsAHZGCvaZCEi4ck5JDpSX9T6RhFgUyaEVfB92wtw3sD4QYuB69onLui0/gr732SRM7SGSgXkgHxeHUgUmIQULpfFnP/8x0KkCr+yrsC2wsed9Zq3jPrVz6Ijqj8weDqLT3hDB9xx4n1UHJ9KzNt91CfC6fpekgJtPnuA9H3oQH7k8wZe+6YvwO3/uX+BlL3oCt589wa3zExAxCsUolIZSDEUMUvV9ySMS1LNvlnlJUuLnI9Ml89twxsc67wEOYSAJ/Rap9+ienatugVAoxuk9a5y9uMAHf+sU//Xv/W286oHvxMNnC9xZM26tS1xwCWaGtl2rAoGIzF+bt8qsGHrDF9t1tY6kYO++fjr5ncVnnfGZA51prBztdJsN/6onSzS/mYKC+f0Ke73ySJf7fgcrPKEex01+Ep+AT96orLsC8xVXCi5Khb/3Sy/DsyvCPUvGQycaS9sZl0zQsC8JFBzDQ1W5a6wPsfjcth/rS5RXkg7eUOcny9Az/ZgIlTf1Hv1TMq17v1Vn7n3WbH7blQbWDJQ2WMdSAQsCFgr4yAXwyV/+T/EpX7ZG8Tkfj/v+8APQH3wWAEAnyhS4UCClmg/g/x0CeW3js+jQUoNK6t65RDRVIfURzMtC7zLzN6MH7ob+5I/HC//C4/iud34vXvWCZ3DXcoX33bwbj18scV4usGJCaV+hAlCQuY0CV7cJ3S321lM/W25/ohPn3Tl5n4pPewOEvLTR7gLX+sf9c40y7CGxVGR+J0XmNzR/uTrmftulYhQEAGcA7sGSXopffH+5y6JvhENp4ZOQAs2EEwXcs2Q877TE869dYKk0mAkrVlhpMuTAEoQucAbD8vNpNqb9ZGixBtt37BvbFOO/L1dO2ZGFCATDkQLTeWsQlsS4XmicFRp3L5ZYLtf40G8oPO/kPdBPXaK8aX45StTESqVJvZjEZIX8EcN+r46H8hefaeB9t4J96Wnke0iRuLoyVWXntQY9dRvqZAF+34fx7Oo5+ND5GR7UCjdWC9xYKVxoqogmYEhmQW7QICi0OV2Sg8WOd0xSYm0gNJBz5vfYvXMG/n0c9GNwRZXNsCYFRvEp7ARiQQyAQMpoJuclQYPwdHlrJ2W/SpiEFNwpTSO9Z8F4+HSF+08vUSiNdalwXi5AUFhpBSauGplftxvtmziYJgbNVGWgvSu7CEZuH5uecYfvkduAc9O5Pjd2v6FkwfejqPv2dn6y39fMKMh03ms2culSAWeFxqliKDCu37fCm9/1Atz3rvdXMrwqbO4a4JJaZoyaFDRPxCb2LVOPEsfdZzct8Y/7BEFOfuXD+vdOEIetk4pdEYXWuws4y1gywO4zA3ypQXdK0B8/jtW7b+DG5cP4wJ0llsS1MmAHicLOKhd2QCls3xB7xX3VAB+SWMi+pH28eT9fEfAJwaYD/gFxgSh8tcCQAvPbnijGWcE4VYyVJjy7JnzkgvAUPbXrYg9GzuR2HzAJKbi0tbmw76DUhFIXWGmF87LARamwYgJbpWBsthub6dbfA9JgJK+usnXdCwj30ZuYNZMqd+z4gPqY87uYxlyTiDXDKkHWPATGghQUaZRMWN6tcXNdYH1HYXFNG0LgBmQALNRB54DnSAJ575oDtmuigG+EzEdu28xcp1fUTKPZEAR7zjyfuR8pak97NHerC6EfZxOTSAyxSjBmQ4s5HSpKj9A+Iah8jRh8vsb6WcZFqXGrLHCpTeZEZl6grDKwpFolUNQ28zVumfQzyH8fXYN4yNQG1H4Euvou06XvESthl216F4RhSC1WnsmALEFYEGNJhhAsFVuzJOHmGljR5cgl3w4Y+yPqdWHS1QeagQtLBJzp4Pa6sFKgMR8waum5K69cNGevXj5e2rCdcHhZ+jD+XG/mVKcX6n9DuY5NCqrJIFuZz87610xYMWGt3bsmnFmpl8gOzjADsCqs2cAOvgyACrbfScza4wXJdTqsPPydZA1LQmDvX3JNAByJcPfwGECUHLgXpqga7BrkIDSQ+i95TJIwFglI9WbynIrc0w381tmEhekAms1vowGU2jpkGWJ5oQnnWuFSm+8AoAm2PphfjwGUI5sHN81NBjmq1DB7zvmYymNAg3s2XmFVR126hC9CnSZetrGqRFc1TZ1P0jQG1pb+r7Qp752ScF4C52uGxgH7FByIvDMZKSDbX6404bwsoJlwqRXOtcJFaQYOoHY4BOIvLfddpgbDPky/cTyzi4jN/Mdgh7LT8ydmTl71B0efSNQybPw+fSptJYeCTJ62My8ZWGtgxaYDU2CwtfkqMHhtBllVMOikLjcr+/u5gbvg/BUfqXShH8CmDy4FlDN+qxr0WjLY2VtucO2+o3rXbSbu1AHWxrZUqTnSG2/NgDbq0koDl9r4Eqy08VUB3KBKgJ1BVv6MA4ucVt26G0SXQ2LMETHlhGiO5/su+Gn8c7E0MeiMvsJllkqSmrAYPk0VtdM2T2V+WjCRJe6Ec21IwUrzxisldod574OqQRnnM4WSDfO/KAmXmrAWtraY9LbpvRXVg6GPtoKQbty5iFXZTZeXkRg+ZsM3AAAgAElEQVSnJFxnKPMnaj+Peyc5A3/sN4h1kp5y7uVlryXjIc7arEagBUMtbR3QAGmuZu19ln92phXmh6xrG86G8XQt50XvWgrkk7zXFBgrAMZYO1daDdUtX2U5MioCr7XxK7Hk8kIbxcmtaHFZGFWJjD8B17bpIYiJUERxMp56DV2+BkD34N818A9dkRCd+AxUYTuPe4qHTEssVyFQ9blk01cYUcmQvvOScKmBNVf04eDgj3P7jMlIgfMnsKZDlEzVPy0E2crxpmGHy79PjnzsJ2G0TQz+gKdBvdfD+5JgI7+R+v9Y/l1KQeNcRD3pcy153xUYcOxe2IAVDEd2DmHMJu6AMx1UA0TiNycxs/ePeQWvkdF3NEwK1cDXVgqi94iWt4MQHLoi4IFFRSJrfmmddypB5VcAsGTsa1QKgoZxMHQOq3LVAWDrnjb+Am4w4USdjYEQryb+z9YaYCPXpQbmrtl/12Cfe6yrHLE0OfAnWa4qhyZfMeXWJS1Qm0dksyuZqgmdIqMalQyUh8kHKsxKATX/No5ze1CRo0LaOUjkFekIXEfhPocGfIkgaciRDv0kgWK7exX+PUYiPqFy5voZFOJgTnGCRECUr4BZTVIQwGQ6dOc8RO56XV1cQwwULRs1wjP9yvmwgwi0ZGoJ1SQGrIQ5QzfJCMnZ8iYrBnZBCDadokQ6Yw7k6/tSSKfCigw4QiDVAhjSwEyVX8HaTiLkLMsNKu1ZMPWOlhlLTWi+sgDPsffMy3fIMsYhx/qYE2LP3lVV/Orrk4FY7ZbkQRKAgurv/vtwJoY1A6U+VI2gxpVXChSsY5nlR0QMRWRnjYCsPppN5WBxbS5ibFSWwe/DC0+Easfp7u64XZljiD1LVTE2GBti40pXg8xBVD2IEAElzmk2nsMlA0SERXWe2x22GKyZA5Jy4nkkWrwsZC5wzoX+UkeRR4MYdKkFMINd5WhIkXMhpGwtY2FI75PZ44aIQMhZkuVIxWioBA21YG2SkXX2dL8Rox9xdvUrRPBzkPpFxvzJ3MwYaA+E/vnUMVk2HzHi4Z+LXZ9Cl/NxVzldGkkEXD/qN6WqyvDmXHzXcET3EDCpo6EbMBQxFBtCoIis83D9hupJ2LCW5w+8/oAVWq8vQ2r6/WEovd9BxW3o7ROyMsgGMBSptxS1+WU6TXVd7zt2yferiCovasVGFCisL0GVr+Jqva6bNUIQgsZ7zhioGsWVhEBcWw00rR6xziNEDNwqCZcHOU+oygHRIwZi9KjOhdjntnuHgVOsIAEAukeakhuMiyUJ8Emgm/laZ8QUEZCmK2ocrwnBWG4UofvGjocGdkAO/mzTUXXeJwYyL3c+RBrkPWJpYiQihxD09X2SKmYoP/dcgSbSmgzGIM/TAVOD2XwAJ92bymCCUhBKYmiqB2Vta4uGmcEPvVfje9V5cMOUICFtkNQ4Ti1lu5VInve+u2eQjcuMDWmy0BfpeDhD32P8uhAZAOp3DACauYpFT5YYNEmDc4KUgzc3BgvpfBGKQ9BCQlmQykC9X4N7ELb+DW7AF8SgytsSA/ng1RQHbWLgztuH7bK1txDq7zbUTKMDew4y9PGs/IU6UP91xECMEPK7gBzsK25JtZlQnm+9woH9cEgdiPlbxpZAN/uVmhyYdPVZOUj6RCP13ScGIYKQIhY+hjhDu2tiJs4clYW8vyGEIlYeGq68UhC0a1sTgqmo5g0Zprg5//Nn97VnK7fK0rRdkTd750Ya30Tgs73oknNvDCjQngXFyEIuugb/MRtRiAw0v9fSoFEKGNqGLo2pNSE0fAXEAA4gOGA44hAMWuTS+ITAfRbEwByrZzTSv6Bxzq3F94iBKXOAHLgypB7awg/OBEw0qHchcVmrPL6LfaSxtVQCeJ8luaP6r7J9hpxZS0Lg2rfyrnUo0B7scnhWlGQEjjXEoEhfk0MQfHIQGvi7vvvHJWIKwhhIkYPaP8BXeCYqzB6CsTG/3xomDV5EYEsEzLzcmRBIyNSmArcH9BBS8lR9T3mOgwOSIyIujU8SXBog1IiapCGWL4BWLJfQCgQ10lZ4OQSgi3hFfPHqzwFCIG9L9pxRiQj/P3tvG2tbkp4HPW/VWnvvc8495370vT3dPT32jD1xDImdYOwgAT9AUaTAj0QiMbYEigIGWQishET8QBCDbH4EgiMcyB/LBAECGYSEZEQkkBD8QLGRbYw9eJSM255pT4+ne7pv33vu+dp7r1X18qO+3qpVa+21zzm3557G79W5e+/1UavWWlX1PvW8HwXOB2wFl3sge3aqPlTlzn0TNxdW3RPgIJbBw+Oysj0wGFxvQtK5OTBw+yrgABh6rpUinfJq8hKU++RpU9cb+Guk5xd+y+c4zEcgtod3F1kiBocshv5wEn/l75gRD0MmK6ti0SZuI6dBxgSW1xPfa2NZ6RBYmhdqwGIKCOySfdiCm8ou5mCXTE3g7jqI+P91muMMFfvMhVIUucQj7ntOjZE4JohU1LW2XQKBUJbbN2QKXJmBF8gBSnnd2msMHXJYZg5AZNnxxHBsqPuYEtshczvdPgPgnLw68nnW3pWpNHySjoaV2P+w9gAXycr2ShokZv5TMpmz4LraIpRZAwfA7ka7z4i95+B+I6YBqCNFUWZtKfTBFaOpgLPjBucLcWAyMYuSKWiUAASEzJm4tiiSpP3H+s1YuxldX2Oi346GJYf92YTIl4EwQaGB0g+fY0BgH7bgOnIdq1fGwGGeGaEmnxCWeenyB0wBxuliReOxrul3rsjlbH4s77dUTKUCqyH51HFYsAYlMs0RfLq3XOSA5Y5P13f7KwBI1CWWOwMg7IPAb9slp+qwWQkDIzEaxURSfjsp8X4CGBi7XqQYxmbQ+48yGciorZ3g6zTrOZcj3Qg4cOWW2mpObXfLXkr/pqPSDjBwa9fxIn2SIgAofsvXWXtnMcSU632udty+MhfYl9Gzdf+AOjAAciW/L1uwS3YtCz0lRbMflDnlbzAVwSWP+zTIXbmPnXqDiH54zrbbkEAJBspf/kUbX0UJVRXTtxFjTjoBzugEUwML8/yBKzj235ZMec+m9+M+SW7LDsRw4yRFkcwC8k/uu4kEoFK/tlx/YY9CKw+dLQ/+9pXZZdiRv1l15/G/WI/iWhA+A0VY6eD40euWbCKSwyoN2xeQgOYcJzQqzptz3HUo8LKdjpVRN8uF35z9lsfclmQmjKIZXXfcGGtqofzAdsxt+tXoiDsz3x4K7/n37ZI5Q92/PXPbLHHjS4ViRg4IpEOR3AbUQUApo8sXi3qUx9Ua4XVWJLsJIpyr8AM4mAMSrqUb9jh2rkwO3hMtMSju7G9fYFAepziviypYglL7hLrcIv1SU/JTf5My9wVPKf0ZDXeSISiOqwGEuSKZgDLKoOY8HESBs78x2QUMau30Jr4ItTKngMFtSLj7NNblv4E6IKjJvs2mbIrldVh8yl1lmfH3SL3uijgwRHv9fbtk1HxARP8MgH8WwGeJ6G+JXSdwSUlvRYbomAczzET3OxtjsP2HB1dmMxuzX+0DCPZZXjk/b8ZBI3ITn8N9HHyqofO7zime6b6zGCreTzqxftNOQfPoTD7mFGBcHxhAAIGxa4z9nqSD5lXn1mTOy3uJsrfi9+8OyoVzsv9eU4lEOesUwQFylmA0uymGKxbKagDzFI4kjG6ioEozxVh5cnybkvpEZl5dppZx3qec8tgxixrz0GxSfpd3fEfY9k+dTPkU/D6AXwXwZwD8mth+BuDfnCp0Lu1WAoKRydmsjpjH/bJvZOQcCmUjLQBBLfIgyG2AgJKxsDs64qD8ykA5NQMqgcWud7HzueLmOm6U2dnxMCIgmHDvLgf6WhnAzOciWAJpNtgJBu5uPpXbl10eZb4zU8WxNDuMwqwf3tGQsn01QDDmuR4XAwNV20Np669VuXILN5Jd7fY6MjUW1ViCeN4e5cjyxt5yDSCU40jwJwj+FNJJslbvufV6leWuVH8UFDDzbwD4DSL6b5j51piBUiQgkI6BcmbJ7PMYFGzBmATmIAMGQhg0CgZuwxwwarqY6rQDGm98YN0HKJSrJ34SMub8peSUb6dL88TDkorFjg+wNftw9jx2RRxIQDDwhp0475OWXVrqk4pJu+Y1SkZGJsACEjsAeNMBhqaoKlYTs1BX7jhrkK49DyjfpkTi5BZf0xQIAISdv9het+NPX6MU+Sris6dIDrlEdZSbEOZe32ICSb7KMs8690rInOiD3yYaTvWY+bumTop0XxYVkPCl9CFwv3NAkKFKSqlxx0RWkCAVfVpzoWQH5ixUkvbfTKMOzBOy7D0AwWj5WX6EaYDwssCBjCwpo0wmTQzFyBSUxKTSngkMSql6qBe+BIPrjlFYwPgaBzPkxuGCUl4VYLCnVNeq8BJYAscUBPNiecxU1lJ/DI0DA2DYbspHeRts2U2lfHU88n1KqUaWtAIIpsrfV8qxOJQvuwpjnCWogYrbZlU+aWHcHb+IOaDgB8X3FYAfBvBon4tI+1geapiiBMYAAeA7qV8/ATQMaSzbS/idwIEDBsAQEEx1qH2AQIaIb9CAxwDBmM1uqowpcLAPMLjtQTE81ylHtLJ+U8pjX2AwVwZ+BpnD4c2RlSzjVgHCmHySjMHE88ne18gxWRQLXBtkJNNBMBvkuU3S5cO5EOfFqglzAjAODrJbwssDBrehLDLnwcq2uK9mPpgJCErGtSaq6Lj5WOy+lGxBaC7h847r/1F5GcCGiP40gJ+FS97588z810eO+3MA/gcAP8TMvzpV5k5QwMxPi03/CRH9GoCf3Flh1DtSLadAaTIokwhJYGCjkh93xsmdWNyxpYx1nl0RCVMy5/h9B4GhE2T+e2z8nQIH+wCD8Yi9YblU+e7oXx9eGmP4i/r433Mc/vaVW4kYmAIEY89xZtu5NUAwh/t+WcBg3zJn1DVGHvnuO2AJRswHNXCwizUAclBZq97Lmuld10dhru2/duzY9WpFzgEDY8cGkJA44nrdBqYecfArSHBdQ+haDPBkiUQawN8G8KcAvAfgV4joF5n5y8VxxwD+EoD/a065O0EBEf2A+KngmIPd5824eEiJ667D8QKllJ64tQRIQXJfgXR8YirGgcRkNMLoXWC03p+U7PLteqXF8pABUGJknhIxml6LLbjGS5sNCOS+ifrcKkPwkvnJsfwE9brs1ygHzBACi5gYRjfLD3/JnJBbd5KiL5MDAfNYg12RAa+q1MY+KbvMBuUp+4CB8ToFJphSjgLPFsC/0+BwKNmhMozy0yAvgSn4EwDeYebfBQAi+gUAfxbAl4vjfhrAfwjg35pT6Bzzwc+I7z2ArwH45+cUDiS/gRr9XfM7mCoHTLD+kyrYM2/gUsFzwRpU7GwjgGDfrF6xvjPPK6WcvbwqUuaKkNsBoMwMqdjdh7MLc/IfUeL5TjzczK9gsmKpLAkMynLGXsho+dbPbrK4WKRVEW9Bvi2A4JOcdt0CWnURCASDmukxP672PSwHPmZS2OWE+O0ABjVe8yYEz9h51wUE10nHHhzES3OtNCEAyXzw6o2ANxPGS2lHnwXwdfH7PQD/mDzAT+o/x8z/MxHdDihg5n96n1pKqWYfBGCQWILMZCCOK3MPlGVMeeuWvgAu2iAg1uk6jwGCfdMOy/jcXbJrdltSa2P795GbmA7GTi2B2tQgPnq/ZVKhuSIcDwHk6ybMBRiAYy5A+bEvgYqZlYzotuUGgGA6rbErt+rIWuODZ0hmOkDq82USoBC6GKRMORz3FT5FQfHeBBjsk+egJnNYrcCEzM49MPJ9HxkDBDddWjmLHCO/guXg2rlfQfIB2//ar5pc4x4eE5G0//8cM//c3JOJSAH4mwD+4j4XnWM+eA3Avwfgn4R7V/8ngJ+q+BrMlrkpiEtgENiCMGsw7LaZLIqgPuNPjobDa9ccCucAgnJfddlQ7M8ajLEF19FLNX+C24g+KCNH5HYUph55uapiHhlx5yjxzEehYA1COdl193kZki2IvGfBFoRbH3umNdA6J1j8NuW2wMDEPtkPqv4qM+tQOzf4FEy9ut3rHnBkDUJ1JDAI26aAwZjMbdJTUpvoTJk69wYLhelgiiWYOv+6Et5rAAZhm0V+L7V8BSzKuMtyjep/xMw/OLH/GwA+J36/7bcFOQbwRwH8H+Se+RsAfpGI/syUs+GcIfIXAHwI4M8B+PP++3+366Ra/u58/3BbPavW8ECKZY9fo+acZxnB3S37m5J9GuLYsXOyzw5ToE6nad0lY+dPAQJV+QNy00F5uowekdvKLHPu/AogK0DV5FoE5bVV/j38lTeQJSKi8b+sXnH0lN85vsxB6mEe+Svvt+asclvu52N/e8pkiuKZmW4mgfTM+/XWJvddMAYKaYXEUmrtN4tWkNFOFHwXELcB48zDPlLrS1XWbaT82uZQ330mB/s7Sk/30cHxM//KckJXGqvvWPO9q2sfBBPJPn8z5FcA/CEi+gIRLQD8KIBfjNdkPmXmx8z8eWb+PIBfBjAJCIB5PgVvMvNPi9//ARH9yJwa5zPJIa1c8yWQrzxO/CrA4LoJP/YJ7buOTHn172INanRi6Qg1JlMAYs6iLKPl1gbewSqW6bNWiwwMzHzou4BBnmWw2Fc7QYnncI0XH80RsQH52U5xx2P+BlUwUMonyJFea00CyQzUWILCZlb2BWZxTOX6sszSedCAqu1YRQBaF0nxh/4l+1QYP0pzQi0LYnav15SaySHVa5wteFlBI7uKnJro7CNTY19QmAH03GnH6THh22c6mLknon8DwP8CZ435O8z8W0T0UwB+lZl/cbqEuswBBf8rEf0ogP/e//7zvhI3FmaadDKsAQQpYyaEXeOvqhw3cKBDPpDcpkw5L40nVNmvErcJBCRLIBV8uU6FFecrBixJJmF8EZvsWhlF4T+LCIPaDWX3q91H1bSjKisf7hrhopOhqGOc0iB7YCVIGJQz2HbzxnXdRYf2khogGHPAERpg34RZXFktEZjuM7XjpYSoBQBxMcYpf4OsDNGISoAwV/bJnCi3B6ByW1J7bdn+mQPdnPdQe+8WflxAbkLY5VX4afAnAF6OhZCZ/y6Av1tsq6YLYOZ/ak6Zc0DBvwrgLwP4r/1vDeCCiH7cXYdPpk4OSmTqvcpshTSSvdDuiFAYa1dzGNBsAlhux/7AYA5boIptpewzO9llEqhunzm41fw/ZNIpeQ33vHb08pcQuznGANRqIUFHCQ7mKtfyuPwZXW+E+0QU+76yYzYPTLTNYmq4qw1XFaf3T4ngH94MIGj+XamOB9sDlpsBDnhwbl7JuVFCtf4ZmAFXznCMKZkBaTYIM+laWPZtAolqoqM9j58ChdKHQIf3gcQWhN/y865KYEPugsyJPji+rYtVVx4sFyGqAQIgOq+VOmUYy5w8XK8TTliWL4FBXs+Zhc+QfQDCPmXFbXucPw4C0my/zELp6pjAXBzAWNhuqV72LJkzTfQ3OSeEcWw9g7oTZF7nmvLOPfN5sg47lf+rCA68lPeZvg/3k3xnEhyMOCeWQvHT9e+a0ozfR8so3p1wNJwDDoBdbCJfa6AvF2YqJx8lWzDmcBj6maTb96mPPHSwPswegGDOWBgdDZHYAk0EixSFICMPPm3WA+DuAJs50Qf/GzP/yV3b9hFpli079C6GVbK1LwN51UIJa0p7DP3fVG46kR4DANdRyDKZSM2PQAKC8KlGunN0PJ1AKKNrHlSUThZyWJw/9hAHYGAfW0rFdDCm4CfD92bKt4s5qAGaQV1kCGLl3YTfZY6Hmj9B+axqiv+2FEQeJSOmpkjgAAAUy+OGjIAcd7TYNbuHSZOEr0dI/SuBQUju80nLXEAw6m9QZX0qPibXqt3dlTvPFBDRCsAhXKzkQ6S+eQKXNGG2KBr38Z8a+2qJhYDxbIa7ZJ8Fc8bYg6nwo09qFcIgNf06pvyv67gz5kdQAoLcp4BB7AZeEnWaVYcboyJfr+ylDaZ4/pjqAxwKp3NYTMuoMNLOAQO3quxvc5Tx9zRZP3G9LASxVPbCHJMBA1TO+QREvuaUpyC0yRwc+J1ZdTXy+87KGxnZxl6NNEnIEEiiITD4pESyBDcBBFPNMWMzEN1+UlmFaYTFeVnUwh1dJZEx1GOvqkwxBT8O50vwFoD/W2x/AeA/u+mFQ0KhMceg/LgbXivMBmZ48Nec03Y5PI5d77pRCIN6TVS7BAFz9dz8ayeFXgMEJWDieF4O3qJD6SfQL8YYgQwEjNVj6mFbBknjpxIgwaZ3weXMGBUleEsK/TaUB0kj7j7XHANB0tGwwuiMlmuTiVG2O1nPGI6I6yW5Kl9vUIgusx75bRP9swIY6v4QdYZBWi9kpENap+T2LUhlKGC4vvs9DghuAwyUxwW/kAAEytwEU6/0FbaszZI7zxQw888C+Fki+glm/k9v64IWOWKaelASENRWNRyUzUMQ4RJkjNN/mYhRZg5AqNaZ69+L4mcDjbnmgCl9N5YZco7IsNIACMp1KmT5RjiGDepYVlTILsVRm42y9Y5VqjKYCEBA6SZ20hWTKYzjbDo2SgcSIkBw9YsmhmJbzWv/VmaENxwtuTTDjDyCrK7SbDDmzVsAgylzC1nZ9zwTs6d1+TqhbDmLULAHY+egGEOo/ggGgKHwVRgAA6K920MoJ+VhSZ/ZcaLOJSDIZ+J1mQsGRofWat2dX0Gt3E9dSOIdkjnRB6dE9BfKjcz8X72E+qTyM+BAoiHPPAc5ICg7hNwWGqARSF5mPwNyO2OQMZpvlJFg4cwkj6+z29U61qRme62tRHkdqS1tHeqYmxW8g5A3HVB0/qwnLarKDp+ATEq6xVbSE1cAAUmq45pCnhmAohwg+LLdtqTYwt0TAoswYSy+gYK/LiVfNo8SJGQywn4MnAzDsWVkx446liGJgG97fHtrguwiiaKyBA2AbTo2L2UsgkkChjCGlOsxlM1BwY8JEyaImuIvJVDvJSCYAwb28ReY07vl+B3uLzoYIpkTJAy8KzPruXJX7mcOKPgh8X0F4E/CmRNuDArGOp2k8Wwl/8AupkECgrwzhP3jzIHMeBbob8IQJAB5Jx8rb1g3MSMSXGK2iEsFJJTgYHrA4uzYa3v9i3oGQFCyA7KqhML/QoIqsAMMpcc/eSUkHfkwDrh2Ss02UwKCW5mGhPsvGKwKOAjMgRzw2HOn2X3OUOp7K/6dURsVhb5Tceflz3IyrJ1fNnSbZtaB1LntCeNUeWFfUNJT/aa2L3c6zH4ASLcbJxg+SioSWJyzBbVXUY4tYyyBBAT5sQkQZPtmMgHFLRXHj59QY1+kL0GoT+lEXrIad1UY88DTqyBzQhJ/Qv4mogdwqY8/MZEKPTMpyGOKJx4AQew0YfCe6AxEgAkz3NRv3e9ixheuUatrrMNEK6iBjrB9EiQIcBCAwVQoz15OfrV6hnIKQEDiu5RgPggJpSavu8ODcwAMBiOkv+eK0qmaIwIgiOGL8x/K6AJGXuFn4MAfSyrnCci92GT2CGxCUIa7/BCm5LoMQ2HHqs74hUzN+KV3+VxfgrkhiteVsQiGKfbKMg2U0z7XcudJpi4iebfPt2sFjsBgjpTH7XNeAATJv6pgCipgY3e58x+ONM3IvASzzp1/6Ksr/OliCkq5APCFXQdNrXLo9u+vqEpAUDoQ5g6K6ToWFBWwBAUDk4JQ0kCgLf02IihOzk4Qx5TXlnUK1wxCxfFlWcGEIZmJMLOIcb6AAAI5MHBl15/9dVjzmrmAkA+qgvBw5oIiLWtMOrPrWns4pOWrGFb8CooGFgFAaUKY0witYDiCLX1wncQcZKyB9zmQPAGJX8GcMAYM6vXZXeVSpgBGNUJgwgegBlxY9K/hYkjY+fJTOQkc38TKI2fysjplv6i/fnfMXHBQK0OeF5YVl32iBLySLTADCJNLaTqYYglKQBBeHXNl/Ivl7aPsZx+aPafkaOhGsCwig5I5spS7uvYBEMaAV1/m5Cn4n5DejwLwDyOlPP62SM1fQIIAd0zODBguj6PcpCDel9AfUQESJ5BARKiNo3O8d2W7UEjMBPkBozRfSIAQzRWUl+VC/zjrUOGewj4pZWrpKT00xg7UZl6Wacdwht0jvRp3dAOE8qjNREu/glIZyWgEwRiEC0rmIGMGNAQC5QQSbG3gqrAGVGcNdgGDgXPeyzAviHNKoDX3OmW7D8Bgii2o6h2Lqk9BKWM4bmDeE31hysdm6LSbOlgYJybdQGI/rNdJAgMg7281tiCsCimFi8/wvZwI7QIENTAQQMC+M9m5h8vxCEgpjqUvgQXipCuv2351elWlfFevssxhCv5j8b0H8C4zv/eS6hNlSGfVsxSWJoIaPZZAQQICpugc5fUSIEBEspoKgBBOnrqPyrYwThoIBcgJJNQAQri/MXBQAgPJFoTwTxSzL/m9mZFGeowpiOV5kwGJZzTHn2EuQ3BtKRmD8EeUfpfHhS+yYVgGE7vRyzIiT5MeemINbAAC6VwKjok+M40EBgilTQGDCfR2G3H/VXCw41q5eUdQa+LYOX4KpdQiW9zv+nFShko+9aPxY9J3SXU7sJtYg5pfU3leTfaJUZfjlis3Z0LLyUw5fu0CBGF7WWa6j9lVHUgNGCUonF9De7akTNZ4HRb5LsgdwQSzQMHvAfgj/vuXrwMISmfB+bYwqips6URYAgIjUDIQ9rtyej+zM5x3plrHkqBAU/okEBoFr/SSkpwSLo6RDjUBDAACJBQAAXGQmgAHnGY4If+De+4czQwRGBQLUc25j8AO1AbYeF/hmRS9nHwEggpIYfQilW3BkU04tGUzUWDoV7CLrg6AoFE5YzBVt8AQ+E8JDqj39UoPIfwagIbARgQ4MIhOCDyD5RgFEJuKvK9CyU6F/O0jozP7ER+CKAGhemBQNSPsEJmnAAiMFnlwTJCmtl1ll06KpdNt7gQ7PNfdbgDZhMAalIxBPluSyoEAACAASURBVGZMV6rmCyXNjKXvk6tB2ifHqHK8Cr9LxR+ZUjsEA7JsV8bN1VYtHTMABJ+nwBbo4FAZmo3/HtiCWKc99MWrLnflPqYyGp4A+HkA/yiA3/Cb/zgR/RqAH2PmF3MuMNZIbiolIDChQ2DICoRjDSdAIL+XYTtAYgLIgwFNQKPcpzEBKFA8FtgNDtyx7iIy/FGeGCZayVMZMRUqRLIn14ko5lOQIUzOmScHBnJ7AAZWOA/mA2jeeiUFOxXRoIjiwjWzRXpcIh/saxOvUdu15CKr14GgdwrGIP5N2C6YAWsTOOjdd4Z1vchW4kE8KzDphKjIZUa0nMBNQI2hUWp/v7I62XXCPU3c/zVlNLdADRDI3wIYSDPCrtTQPOKcOkXfT0mtzUorEpCb08Lj1/E3wSIAYqC3ARgkcwCQXtV1XsHAR0BsLydGckwbTGxYnpszBAEQDMFFAglBdrkUTC7AVmEQyU/gAjAI95Huv56v4NMmdyWAYoop+FsAvgzgR5ldlyUiAvDX4DIa/oV9LsQYTG5mUcuDcgSaBhJDIAGBYRoqfuRgwFigZ8AUnSfWLQMFhFa58wI4MJyzCe5+ktTsm4CrW6mAawBBgoP0nQasQeihinlgTpDAYFeugADaa6xBmdVQbgvi/AmSPb0Umkh1vVM8S7CrU5WKsyqeJYhAoFEJDEQnxMrQbi3YKt+ITNAQbnZPoXUX/gTxJYVn6q7BAVgUiY/KtMlO01D6HsxBXFDyNZagcBqclD0YhsE7EIBgn+yFY/UQ0Xq3Fz2KHBCMRdQEiWQMExAYMg8OoMLrCf1LhD1zYRIsyxPbqiGAxVgWWk6N4QQEOyDKD7/lmObOywEBg7N6l/WblKLu8pWbAqw7IBCAR2Jaoq8FpXwFoQ7SAnV33Qpzqem/V1WmQME/wcx/UW5gZgbwU0T023MKn7K/XaezZ50KCR0Hk0EABL0Vyp9zdoAZ6BgwlsU+zjoXIE0HBK3csa0ixxRwMikoimN1RllK0CCfRclUl2ldI2LPmAL53bEDsmO54wmKOUYo1IABwKixBZgADcM0x/538V7GVnKrFDiyvb55qOyEEvLJ4mnifHndwA5QAAONArT2ZgRhSogoT0xvrUqmg54cqgzl9e47G8EkSEfEOBJC7PPgITAHBDE6OuCQgQQHy+P9cpjOxoYVjp1+njUZvPm5o1cBCMKnAyw5WzCrHiPXrTneAeO3mMB6fVXPMqKG4nn5RbRv0wF/pdsIFfJ9qZikOOyQ3uvY4yxNoPDXCcrdcmEiLb5nSj77zbMAQR6NUIw/E892cB9lIqeKUg/gwLIbQ2vsQOlLIEHOp0HuvPlgh8xW6bXnkNnzSuZxxoNLHYhiJ5KAoPedIX73n4wEBroICvIOkmYRARQA2gMCZkajCEyAoQQMAoCQ+lwq7NyJqf4spsBBtLOhDgwoHL8HMNinq0lAUKVcAxiYyF64V2bDijADA5pa7o/KiAeNKoUjwj3I4EsQAEGj3TYKwEDVG6a1gDGuvN44cKAUoIxjDRSBe4voiKj87N+bA3KHRF+3qPinQUICBq4uERjUgEKo75xnipx1KP0W5vgqjCYvqjgeliLLZxb9YGftR8obadcSENTAwBgwSOq/rFNOiQczphybwn1IgMDFfohzI0vgx7ISDIz9lsxCMBuEciQgkGOdq4MDA6nOu9tMwqCif3C+ho0ECUpcKxwTrsJIpo3St6AmL9sf+aWKeO6vukyBgr9HRD8J4KeZ0+0Q0V8D8Eu7Cr7t+y87W2jcqfHngKCz6c8ww5uAYZjjNscceO9iUWPlHfmI/Ey65LwUoQHiel01AFAq7LJDlDP9SXAggAHg77kABunYBAx2Pc9dbM2YEqdiGWULjvHl6bxh4QShoEcUfLRDM3JFheF3AiW2oDAGR8WURvuMLUDbgBaNAwNt44BBCQgypsCDgl4BZHxZFtwbjyBNNCfAekfE2G041jf9Gty5vyQhAxG+PNIUR8UYJVCYGwjy+Yy8XFv5XvFbkIs6XceJsTQlzE5mVLSj62bjlCxBAARaANPAJEhGIUhc2Aop+qCE0aH/mIIdkwAhJDyLOUeKW4mzdyQn6TB5YSBjPEO5pTk096NiwQ7k5oUSI44BgjmKy0068vFSjmPMDBJhhwpi0uXHhgEr4E0x8mnKOu87kXnVRAxnr7xMgYKfAPCfA3iHiP4fv+2PA/h1AD+2z0WC0i6ldPgJaLK01ZUSOlJiCSgzFQQwsLWOEehtYAYYJoAE5uhPIIUoDeEKLpGIYmcrDvYy4ynDBgAXeCHcV3m3eQNPHXVMMY/5AWSdTKIEiDp4YJBHJeRlTQGCMFDuOk6WZcGJMcjuwZc5xhJUXnLuxJZYArZCIQ5yFCCbSpASvxUSGGgUsGg8Q+ABwcKDAq0R/QlKeic4GargT+CAQGQNinui3haz7opGKLcXo0YKYfQvltL2cF9cNKLR15WBAc63+WdLBTioAYOp9NP1fAcOuAWHQ6B+PjP5d12/g9rW4pUPjg2AQBfsQAkGxs1iCcw5FxKKjEFDYfadlLNT6iJRmh+jAjiQ9yAnB9HRGaHMZDaQAKFU8tJBWjoWSjNCOmdoMijvU76XGmsQ2AHp9xGODfsie1IBBrFsThkNLSe/Arm/fE7yendV7jxT4KMLfpiIvhsuYRHgQhJ/Z58LjNmZo51PbCvDmUPce6pT6ACJJQgougYItobRWcbWcvzuAIGFiR0hvSmXtZCgiWBB0HBZDFWYqnu6lglgYrAiWJsaeAkMBrP4cB8YmhjSPYfnk4BByRZEO10Y24voBAkMJC03lVpUDpZlXcbP8c9whzUp+VFM9IrKCF+jpcvvBHL2namL+6ki6eBH4E0GARC0rTcheLagZAgUXIaOiRCHDHb1FmhUDgz8iw5OhsEcEH/L+k5JCWJrx9eKKGbtAPKoB6T3SJZjhsjaMtDVahXvJoKI8p1OvKrwuIG6AtjHD0m89ggItG/fjeLMHEYjANiRPQT4PtiI5+DMcyl3SseEzlKc7cc6MPx44rcFpzuhYIP9f5fFJ5oBbE7ycFGO286j5Q3G2eBEecsigcFgH1wdS7+CEjzB/77jeADAp4MpAAB4ELAXEJgSR7nNP74+Q3CdzJkKKDMVSECwMQkQrK3B1hp0MOhhYydQoPhHTL6DKAdmlOvCys9UodgNXBQUcT5LS4OrpBd9ncVAIY8L4CCLQEDONoTyAjCIzyEoWwkOBDCo+W5Ixyt3/0NlXQ/jmn5p+b3KmWsacEsTyWQvkaYDP5MF0kxSMg9jUQckzQWBGWg0KPgQBCDQaAcOdFMwBN4pIEQbQAn2wZcbzAnw75Q5vZAC9UUA0LP7bvxnNkJ7R8TyhliUUdxj+iHqVpNgZvD7y1wJEQSMyIAtEF6wpYkhXxQpsQVTwgVTMEcRlFhStltpMtDiT8XPvH2X5JBziHMxNZb9BEUpwIYoIvfUDFNkCDpO7dsHrEZfICIMUhgPZ/f5fsvJN6o0H7j9w+RE0Xcgljved2UTlSxQCRRUUe8swuAWVLa7B8cQ14BBqOtdFQZnE9BXWa7raDhL5rAEg5S77AbEMRNC+Mmi0QeWoOdxQHBpO6yxxYY26L03gIKCAkFzE78TFBpW0FDojYImhc6zB9oSWqVi3gLDQMshTJHizGRKtEp5D2rgoGZSCPtk2ftQaaVil05Xw+vw6HmlyMj8UreV14/XmuMtZNNn6EelwmBO1971LJyS9Uo8epAKVkB8ciOAQW9cYqJyutxoZD4R1jEDjgu1QwooPJwSEPTuTz44agBuhgxA8i/I7y0M+CTRZq0RhfNHwMUcGY0OGPE5KM0JU86ibAHS42PGPhL9BiqAoCGGVgxNdmA6KNtRcHwMDEpvHXzqvOIyrHMgIa7vIgcIIeR4zNRRvJZsuzQhSGdpubBRuO4ufVN73UTOedr5O/h8DAUwGJ6T/y6PyTBqCJeeqBeHupfPPpRRlEuzBpBXU67R5b4t8lJBwZSUY1YM+yGOwKBkFErKzQpfgs4CG+N8BwIg6C2jY4utNdigxxWtsaYL9OgAAA1aaG6gqU2ggBU6KDTOeIAFa2h24ECD0FsXgdAQRV+GhfcicvomhCnlHTV0phbkxkVhuq7N6oOCloCgSqkW5WdlFJNIOTMqvbCncsOX15IiF6kC5CypnFnMcBqrmAfGvg98Cga/KW2XPgXZg5bRBsoBAu2Zg0D3AyBrARgkfwMk5WY5Ha88IGDlpoaSRfKjvAQEsAzuUdicHK5giOyHEhBEM0T+HEL2wxjVIB51BgAqvgU7nQin9k+wBen63syzQ8b8CTIwvLMUf05UIoiAoFXWAQMPClx/Zd8XOP4OEttyfPwEYxVaq2KdAltpGGASa6Jk5RQE1MhEpxRGcjYsc6rEYwrs6e7djUfJHUU4TPp6xOU8KPk9hEykdmQSAIyzAoOJTAEIIib3/hnh8Dnv8y4zBHdRvm2gAEiKKUhp65JEkrShhbExMgQ2fOf4XdroyHsPWlhYGBh0YFgwLCwZGPQgKChvSPX8ARQUNqyxQAtihQYKmhUa60BCSwoLpdBpwkIRGpWoWJlO1Dk7Bac7t0GaATQlu5nLmsjJTwHIEilJmcNGJV0Y7KhRT+40CdQkzPhL50r2MyNgqEMSQzHCOFRsz2F7DNVjigmMsucgH6S7KTESVS6WKUhR0/igFND4btFTOi76F8S5y0DxZ+UG3pc5cr/sQ2Okkh5TpFPLOoe0z/G7+5Zpz0gZ2+kZflYm4OrJ4rzBdertbmA+8Km0S/+CXesrxFUShaqcg1lqxYU2rsit7eGsSA4QaAEMtLKOUVAOINScYuPs3ypsew1NDYjCgkkO5W+KiYwbq6jIBwDPhPrfletkflRIKYrlSq/yGqU4kJ8yL1LYSvn1Uh0YxpJnCTiLJsjrNtyWXbfMVxC314+vpYUODHG4j1S2BxSTaUtfbbkjRMFkmuMzDFkc9ucsmHkWoMgdWlwYX0mFZ8oipN9liiEsxicQCSGA0nQQnW8iQOA4ICoCWigYYiy5QcdLGHSwZBGW4LT+X7hLuTQnQaGhFmtvXtBoI5vQcIOWG7RWY2UarLSOpgVAdmCOCZA0ERZMPhSIsNSI9Qyfmhgt5UBAIXhRp45sGNHBMDhd1jKpxSGL0nOXg55kJPYROUAwJ0eq4SAXPvM9gyV3BTCoORiWGQ2ze5gT6mb9ugXSWxWIbAErlUwJU9L7VmjZfe8NsO2Brgf7T2xdeCIb6z5tAgZRPIAh5UwG8V4WKnqEhuiD4L4SLf9jM/xeMFThmYV1GQrWihrECI0iiGUUEIyC0ApbQMRVYJCFlI44lo45p82Vqu8leTBAFo12jIEEA0r57dpCKQap3KxAHnz2vcJ200ArV9mQT4DA0KRi5EC08SP5AdjAlYnXWFY1TIMUJWxp4fKrJCfCdJ/DGfrgzqtjQygrTKq09mHa1vVl7QHCvu+i7IoU+3/OEuRmhvwcCQykrrjLjEHQV3dBpqIPjuVvIroH4F8H8OMA/sd9LySVe7S9ISmL8L41ORrLZOfWnRMZOSCIoTJwSpgi4nbat7EKB7z00QfG8QbUw4Jh0KGnHld4gS1fwqKDjz+AgoaiBir8Jg2NBi0vscQKGz7Aum/Q+Obbw6KBwkJpLJSCZeDSuLteaY2FIqy0o9hX2jvPR3ptHBCE0KowuAS2xL0veP8E97+MiCjjvaXZoHxHN5ExaCGZ/KmeXS4TzBzAwPRokDu1eQVlXTuCBbhnUONHv8ammX+toF6WY53S32yB9RboOu/1ZZzz4bYHd/5z3QOdAW9tYgU8Yg2+A7zlCASgAGqoAAruRdMigRMSfgiO8GKnYP0zigAhAIEesAbgnmANwRrAOWoGpc0gzdCteyZKO9NDlTUQ32fFr1dYj/juYj3TYly1VzDXp6Cm6FIZeaSNdHYNfUor6//c90Zb6MaiaSx0a0HaPWflI1uocZksdefOCWCi0RaLrcWB0dhahc56YICQTM2bFny/jM6CGF/QrXdvGaEZaOPu4qrnARMR7gsYMorlTLxUxsEpUZMPo7QAFCOm2BDn1571WJvInBBD3Wr+CagPBzUGIZF/d9SngD8FoCAIET0A8Jfh1jr4bwH8EDM/vclFw2x1GALnBwsESpCi0vdm+yhTg4cbECiagDURlAFWSkOrZcxEaCywNhYdu7+17fAtZXHJz2B4I+rVgFhDUxtBQkNLWHIsQ4ctzqHR0QYXeAbLPVZ0gif2DTzCEQwzfh8foaMNTrYPcIID3GtanFgN2xLQeCdFR40A1tP8DB+yw9426js+u/wJgSGIIVDutqGRwIQ7L5kNBo6dE0CgssSPP2f0lNGyq4PKLl5YHsvl70rdLFycfZjl9gyQdQpv62d0nhUgpQDdA03nFDV1gLXOr0AkK6KrNXB6Dn52DpytwRcd7HkH3ljYNcN2cH9bgukJ3CtRV8/kWIW+V7CW0LYGy3s9miOGPkAc7bj3CnsJn3Y53BOBYZ2fQe+BnwAD5fO0HcF05D+Vm/kZSrS8YjSNBbOBtgAWDmhmpc1lCEZkEhwgf3cB9N2WY3YJCEpxPgSISl0CgnZhoFoL3TBUgwgMYtnWgYTlcY/lcY97ZoMHa4X1ZYv1tsW217jqGlx0LTZWISxmZIRpDZCmBELPQGcVeh9F1TNhawkNM1YtY6Ucd/nhRuMbl4TL3vlKObBPmV9pnEj47Zn7jN8Xxr7wrMICT50NIYx+oKXcWlYqdUaiOcqmOHSUTrP+UC+p+GtqPh6HNHbcYaIAQGD6Xn2ZMh88BvBXAfwIgL8D4B9h5tObXjDQilJKtoCAOFBZpEVmlA9ZSQ5BAsnCNZ6lptj4NREacmFCnXUYUytXWuMmkbjqFdaGcdW7WaLmBooUrPfcUtRCkYJCG0GBRguNFi2WUKywoStscYVL+wxX/VMQadjG4IoeQNM9bK3BR/QuOnuJC/UQp7iP4/4BHvRHONg608NRQzhqFQ40sNKho7r7cMwC41CnDtdZcms4sFxkiSMwcB95UpYx58JdnW1qQaUxFieQqnnExEinKI3Ckb7OExaVaXNdwh0f7malrcuvT5Bc7dOs2psR2FP/5Gf8wZ+Algv3vTfOFHB2Cfv7z9F99RKX7zc4PV3h+eUxrvrGvQMxOzRMGQtGkdlJCWnutz3ePj7HZ954gVVjoQ/cPZoLRw+3yjgGIUQzeP44OiqaxDqwpNPgWYLeAYN+64GIUTBWxZl10xgY6yJpbtXK6U0Imd9A0delP8GoH8nMKtVApgwjtD55l4weGCuHFENr51/gfqe6sE9YGerVHDD0kdNWvLVQLxjbjYaxhK3R+HizxHtXS1z06eZkm8jq66+xsQ4QAD4XApwPxJG2ePNgi4UyaOkATzcNrnqXcr1VbiyToglolRvbQvPpbG6SkKnZw2TLGmea1d5JOilxjpQ/kNuQIfwOAgiLkTDF85bnSzAjnaHH2A0Ux91Vn4JPhfkAwLsAPgTwXwC4BPBj0r7EzH9z34sFZ50wWAbFRGI/kMwCgS1oYn4Ah6zDueEvdAblvX8b5X6vNLCIPdFdJeQxCA31uCWsDeHpGjjtgSVWeEifAzSc3wAW0NyggYZmjcYHLjZQMaTngrc4p3M0aomjxWs45tfwhB/i9eUKXzzRuOgX6J59Pz6kj9BhgzXOcalO8RFagAHVaxx3j/Dk8gEeL5d4uFTYGuDppocC4WSh8GChcH/hzA3BFCOznClygwXA0JwP+EPkPh8QTAl5mpO8yWcswjgLSRyTAAxqrD4DydtfpDbWSbk4D3dx196gy72jfqlnUGeBrQKvjZuNL7bApgOatZsGbp2W5a0Bn21hz3p0zxin76/w7kdv4HfOj/DelcazLbA2+aJazpdF3jO846mTzu+/v1jge09W+GNXK7z9/AUOj7YAgBcvVuh6jYf3L3HwqHO0vjehqNb9UeNBkmHYNWA2fvBeeNOCCSYDNzOVOR0CkA5MgWoZasGu3NJ0IN7Dbc3g902TfO3r+FbJnkkLiYc42PpHWAnL5NNNKLAJLIZ7fs3KQC0Y+oDRPNKghYK9MOieM86eLvH1p/fx/tUBTjuN9640vnbOeLE1LhmauEbwwM+v69jK3jIOGo3HK4WTlnDcugMXyuCw6XGvsThq3Mxe+7FN+i9ZdmPeScu431hoAtaW8NFG4aJP15Oe/wHQy9l43oZzHwAZWSUZGcEHVt+JvJ5U+pmZVG4PxyIHC05X3FHzAW6vL71smQIFfwPpfR9PHDdLYsMIn8J8MEypK2aDIX7WN9CGGIaAhiguYdwm3ywsfIdZKsZxY7HwM0tNjpp70SusfS6ahXLHWQZOWgVFK7zWv+6vQxGRNwGZU1o6OTRcw8Blf4iL/j56+1loAk4WhJMWeGNl8ccePMfaaLy2PMZXTo/w7tUFvq7exZl5H+v+Obr+AgBwuHwdl833wG4+B0UrnHUGX+F3QVB4cvkEr20P8GTV4KihzPknoP/Wzwx6b27QnlFxJlGOQExXnvv13mdhhoDTI5S94/ycnWYHQVtHB8RspknR4SvusxzZgpQpxu9XAMCgDs5WSgCUjSBCLQB13AGNAl/26D4yuPqowcfPDvHB+WM83Sxx2ml83Cl845Lw1bMOv98/x4Y20QeFfUSLRXJedc9H+X9uZtOjwxaXaDcrfOn0O/Fbp8f4/NNjPGgZLTHO/Mzy7Y87vPXBFTRZdFZBE+NktcGjB5dY3e+hlu4++7XC5WkLaxRWhx2alXUzvo2OpgoAUNom6lg7alwvLdpDC7X0fg0QJgmF6Jw4JXJCFwe7kfDEQeSCAHQZazix4JV7pvMluJYYJmjP4Fgmx5IoF1qo/SqObAmmV9GPyRjlqH2j0GiLI95AGwu9dLMJ86zHxXsaz58f4YOzI7x7cYj3NxpnHeF0ixi2HJzrgKEyDmIY2Bpy7YcZK014sACOG4tWMdZGo7MKV0ZhpYG+TTPOFNnk/o4axhtLg+84usJB0+PZZomeD9Bzba2B9Ey1AjR7Z2jOfRUC4xrODRkIkz8EZ+WVkpkwRFTB2Paa3GTi8irJy8ga+TJkytHw37+ti4TQNflywyy/nj2P4vHamwlYsAVRORPD+vA+pxgZKwU8aI1H1r0bCJjQKovnXYvzniKgaHx8cquA1xYWixMFmcZ2qdixDmRd0hN/jZQZzR3n0pu6v40lbLxSWymLVjsP588sDd5faOAK2PA5OnsFa3sQKSyaY5w0b+GhfYKTZoGHSw0CYbE5gEGHDTo86wn95dI7BbmAypXSOFkonCxc9PCGnR/cQpObbTBDNVYAGo5x2lKpl7kG9hHyJp3ShBAAU/AdiQBEjAakMKDApeQOb7nyiCBAEWzvY/oDGDB5GVJBWZNCG5UGmmedo3BPG7z3wWN86dl9fPmFxu+dG3xrs8YVLgEAG2zwXH2IS3oW/UosG7B3U7XsPrP6swWRC28FgN5eYmPO8A3za/gSv47Xzr+AY/sQRziAhsKhavH20QJvHCxAcGxEo4DPrBjfe3aF73zxAifHVyACLi8X+NaLI6yNxsn5FvcP1rBM2HQNmAmLxqDRBm1roDw9rhuGai2alYU+JqiViiEt3PmcCT0AhPwJI++8ZJ4onwWN5itg+Z1y9si/XztDBagKIJXixo5kQnB9U8Gwi2oyVkGB0Rnlj/fX9uGGnXXbNTFWbY/LiwX6Fwr6YwZ+F/jgxT28d3mAi16js8Clcc7ERw3jfgt8z4mbcOSO1OXEx0lnCedG47xvoInxZGGw0jaOWx+sF1hbhbOecN4DZx3wYmvxYutg6HHrxoADDRxowoG2eLha496B84k6uVriyoT78RMH779gOGX2RgtsLeF0SzjvLEwcU8kvKkqRicuzKOaAw4qXXHN0DM+gdIqUR+b+EWm/Y4L/gCl42TLlU/CTE+cxM//0VMFjDj8yZn243nlCnTLPOMJXlb67CUdopo4dOFAWR43FWwdrvH50idWix6bT2PYN7q02+PDsCMARTjuNi57wUafwdANc9U75v7YEjhrgondmhsgUeBARwgVjJID/vlSMlbY4JAvqFZ53Gt9aE34PDb5+9QiKXGKl3joVckgPcK99DUf6BAdY4ZE+wJNVi8crwpsHjO+5d4WzvsEbH7+Nb11ZnHUGF6bHqV3jBZ3jlD4Ew+KReQNvrh+hVQtoIpxuGR9verRK4fFK4dHSDYYAsFCMA22xUBYNXGa3YPN1CPZmnU36K8RtxeAtf4/GqZcOat63IP32bEEBDNjAeeoDWU6D4HlvDaHvFUzvBn4A0IrR9RrPLld4/+oA/++LJX75W1t8Cb+Jj7uvYtudgUjhoH2IQ/0YCg0aWiZQAA8K2OW7CAABACxbEPnrUIuGlljQISwsrjYf4mrzDTzTv41Fcx+L9hiHzSMc4TG+dfYZ3Ds7gIHFOV2gh8Ejvo/vPrqHL54c4vWlQUuMC6PwwVpha4E3Voy3D1xCrkujsFAWry+3eHx4hRO9RrMw0EuTJ7NaEOiocSaJnh0CQXCwBGIyCztvMIvAQLAFY1Kmpd7HtDDFNmVhsnBgQHl63C1mpIbHMYHI+V1sjcZF16CzCg+WWxwfXkER4/xqiY8uD3Datfho2+AbVxpPN8mXRpMbf44a4EBz9FkC8jZfq7smxoGyaFpC60H7807jtFOORbApbHBj3Fi1NowL08OwxUo7QPDaEni0sFhp5zOy7dzQftwYl/7dp2LurXs6IZz5uGE8WlgcaIO1Ufj6VYP3LGFtHNvYKGCpKAtbDeeGlRnLyK9d76c0GwyYAyRAUO67q+IJsjshU+aDi8q2I7gVEl8DMAkKgNw+JZNyJJZg6AznDhguQBL3q/SdgKikD7XFvcbiuO3x1sk5nrx1juaYwVvg6uMGSjt7qlaM55sFvn65wvtrjQ8ue3zYXWFBDa76UGmnNQAAIABJREFUJe61KfRnIcwHytNoyYRA0Z/hqGE8bIHj1qVOPdKMk5awNsCVccc8XLhZxFF7grU5wUnrti39/RA5RuLNVYcv3H+BzmhYvo+vLVt87ZzwwSXQGQPNGks6xJE9wet0H4+XLV4/IBxoh/TNmtH3Bh+tgYuOcLFUeNpqWHYD15OlxZNlj0eLLRbELomZJYDsTqes+HoEiKuvfslAZfteItY7AMSgQ0NgEJ3Bgh3ZAwHTE4xxVHrXaWy6Blddi4uujbObD9ZLvHO+wDsvGF++eI5/wL+EF1dfB8NCqwUatYKiFkTBFKBTAiy2CRh4tgBABAYsbiAoJE0t2uaevxfX/aztsDXngAa26hINXD6Njq9wZZ7hd80Vfv3FEvev3sYDfgMNNHoYrOkcGi0+z5/F546WsAxcGYujpsUXT1p8n9H4vLIOFACwnULXKfRrBqkOje1ALTnnxTWn5628hh/x8biOpBwIhOAsuivUNFRl72shOcwFm3nPBNiERHtLMCo50G6MxpVpcN5rKABvHF1itezwzY9P8O7FEZ5vNS6Nm7GvDXCgnVJc+yyqrSKXYt0STru6ahy7l2D+W3kT10VPuDKObXB/7EKYOShUwtouYRg4aRiPFlssRV6F9y8PYS8IV0bjmTd/vX9FeLp2DMBx63yTGuXu46gxeLzY4sponPYajSJo6+rUKufDEJwWDXNydOQEvIxlkd9geI9ZmCLqgGBqWpKOuZuOhgBw59c+YOafCd+J6BjAXwLwLwH4BQA/M3ZeKfJFl0pErlTmruOVTUjLCUZYgEQKKedwosk5FGliHDUGx02Pe22P43trtA+A5jNLqM/cA/3aU7z3D07w4cVR9Bi/NITL3jX2I7VASwoht81BI/wJKPkQBM9d6cGriH2EA+Hc24WDOWJrgy3Tj68EPF66cx61Fo8WBkSMs07jg43GtzqF99dLvHf1BPcaN2t4fWmwNhoL1eKLuoWmewA+g9dXziTxZLnGdz08xcFqi69++Ai/9PQefu+C8Gxj8Gzb43RL2FiD53yJe1jhe+8f4ntPGhxpg5U2QHzGlJyyQMUMJ60mFzsnSWDg3mb2rjEMKvAvebStzAmBCwNjNCeEjIdiP1tnO+63Gr1xzMCma3C2XeCsa/F02+JZYIs2wFdOt/gt/gre776EbXeGRh/goH2Ipb7vElb5HBUWBpZ7nxEzmQyYrWcMPEtQNljeoPdmCMM9Wn2ERh84xkG5iBYLi85ewVDn0247dqFVB7jafoSzy9/BM/wWlFpCqZU7lxq0zRGeLt7EVy5fxxaXuDRPoajBd5x/P9558Tr+obMV3lj1OG5c3dZWoSXGG+9f4cnxBZbLHtY68HR4tMXyfo/m0L+mCltwo7xCGeMzbmYIIicLwQQ5fuxwm6O6nUWEQoSOVbF99x4kWACXfYNLo3BlNFbawljC5dUSX784wjvnC3y0cUpRwdngFyo5NAMup0BIRtaXr3/ikaTJkQ8t9gzrgQ6mSjfxaSmkZ3bbj32hS+0mWx9sGlyalPY9lH1lCC864KO1xTev1rBgtOoAD5eE4xa4731agrTEeLBwjo7hmQfw83RtsDYWj5YNHi7d+KjCFFgRKKzJsKON1JKsldslSxA232nCgD8d0QcgokcA/gqAfwHAfwngB5j52U0vKm3MQdm47eHCPm83uxkslILy9kDt/xqyMT6/VYx7jfPSXTbOS4o7Z2emB0fQ95/hqmvxzaslznqNi17haxeE9y97XJgemggrrfBkpfBwGTp6LTpCoNz4jNxnzz5RDJClNu18uNFFnzrrQhE2mtAxYUXO9PCwJShSWBvCpXEhiJ892ODBcovvPFzAMOG11QYLbfCtywNc9A0WyuKNw0u8+YfPsPj8AY5/632c/+bbMHwAl3iJcLrtccYbXNAlHtERDptg708LtiC8g8jk5IBg3judv/plrcwp5RAAQGAIxkADW58AxiQzQddrrPsGF9sWZ12LF32DDzYa77wA3rvY4qm9xMfqQ7yw3wQAHK3ewD39OlZ0ggYtAOckmDJg9uh57XxCuI9AQLICPHN6zbAwdhPhlKENiFyCrEYtoajx+zVILQC7dddhG4kYYzc4334TV+opjN2i6y/QmRf4EF/CO+a78Nbl9+Et+wZOmgUOG4WFIry2UvjOowXevjrAvcb4WHXGZ68u8NbqFM09nyBpT7ag9C0YFUuA5gwE8g6TQ012GbxS3D0csCE3geg558M6Jlz2GlfG9ckro/G7Z8e41xictB3+yInF3z9b4ptXrn11FrjsXTvUygGCziLO7mVUAGO+MkjrhuR1Dw6LSkxGNDkQslDs/BJ6B0ZW2m1fKfYMpsUbK8IX7wHPu0MwgDeWBsftNobJXhqFS+PGxqdbZ5IKjstb6yO2fNj2pe1xZDQAij4KyUw8bwnmmsOhzHwo98WxN17hbvoUOOx0N1DBlE/B3wDwzwH4OQDfx8zn171IcEZzlK1Mu8vZ7DNXRq6BuUQ81q1OQM6GpTlk9HMntsrZ0hbaQoFxfrHE9msN9HsW+Hsf4N0Pn+CdsyN8sNF40REueuB061J4HmiNlVb4jnsa33+/x5sHa1z2DU67BlemPlBJUFCboVjmCAiMYnSc5z8PdGFvCZ3PpfBgYXDc2jhABzqQGXiw3GKhDR4eX8IYhXdOT/Cbpwtc9MDnDldYfNng7f4U3VrhycEaf5QJnz904OfjrsXz7QLAfXz2wOLRoosOh6YwF7hJT95wSxYn21eZve2ie6dy+tdkkLSoojxS+tcQkudWuOx67ViCvsHWKmytihnmLnvGt+wZvk5/H5Y73FOv4+Hyc1jyARa8gmIFQz16GLhAVIWeepzCRY1sulP0dr2XQbwGFrJBzjtahG1Eypko2IMBCo5xbq2OnnuQVejoEpoWaPQBGn0AY9fozSmeX3wF2+UZnq8+j2P7BPfXr+ExHWOll9haYG2UaMfsoh1ahlo5PwNnkpkvNUAgfTvc7wTspliHWjO5jikhOB362jg/pOBY6FmxzhIujcLGOpv7aUf49WcL9BZ465DwYMH4aEN4trER+IbliReKcNhQBAdBKQeQ0BWvfOoe4loE4jnKd0CcFKXxIHxDadR0DoG+LABrQ1myIu378LlR+Nqlxjcv3eJxS01YaIKxjBedwUXnfBUaRdj6Sh01hPsLjQOrcOJDownutTLS0u+1PAOlVCdZNNwnv38a5I5YDyaZgr8KYAPg3wXw74gwOIJzNDy5yYXDeucSEOQrlIXlhR2t7agp11JsPMYpUE2MhTLQysKC8PzyAFvjkspsrcbGKLx1sMEX7llPhzkTgvVRCcdth9dPLnD/8RW2FxofPD3G/e0y1quUYQhlOi548gfv4eD53AtlFhwsXS52HjR6d++OLmy0xUHbofE52XXT4w8/fI4v3lc4XG7x4LVLHH9fC/XWE6yutvj+0zN8X38Ke9ph+xGwPm1wcbHA6dUK217jom9w0bvXbkQ+g7QQTeVdifuNJh+B7OfMhPZJXwsMw9V2gYHAEoRkPb3RMFahszq+D02MpbY4bhTePFS47O9j038Ba7rAffsIJzjASjV+lgx0bLFhl3R2QQ2MdfzxpXqKRh9AK99GhNckFR6UvI8X3cT54TPsJwEgiBRadYClvg8A6Bavo1FL3KPXXUQLH7p0W0rh9YMWXzgGvutoi8fLLQ6aHgetc1JcNAb9htCsOT6DgSVkhzLPj80PLBmfUN5tS9lnwwSibINOmTkmL3jjA252/M3LDl+130R/1eOhfQ2P9SFO2gYPlspT+44VOG6dKfB+a3Dc9jj2z/Ksa/Fs22BtcudGWYPSnFo+isBUpuPTPivbfuW88j47S9goB4ivjGMuLQOPlgpvHTI+s+zRKsZpp/G8a6MZIjhdHzaOidha4Lh19x7zvXCYUMxnRUpAUAKADDgULMJdFPaeR3dBpnwKXgpPIxu6XE5XFcox2arZD+gYzGLDcZrYhf6BYVhFQKAIePPoAp95coZ7nzdQ91vw2sBeWRcrvSCo4wbqtQNAtTC/t8bFb2gYVnhydIGT4zXatjK7C7HyauQlC4/3QGe7lMQUB8SQUEYuvBJP92BHaRdC1q4Ytge6Kw3VWnzxBy6g//E/BH78CHR2Djx/AX7/GfjMhSGF3Pmqtbj32Q6Hmw7mqwrfWB9jY5zhMwIjyv064j2WjMHkrC5f2S27l/HTxmUGlVwDBGGlRmMTMHP1c2wSgLh87ko1eLLS+K71W7jokx33qHEzrjAArg1ieBYDePPyC/iafYyrxRpLLLHwZhrl//ZdQEY6H42dG45Jy9G6T03umpoIC61w1FCcqT1aEt44cIlsAB82B+DJwuB7Ts7w1msvsDzqoBvG4pEjIdYf+vTIFxZqgepCVGPvIUqZhbJyPPkpJof+Y+uL9tymyGrGGTlcMjQbqHlvDz9pgS+eLHB89TaebjostMKbhy3eOCA8aBkL5cKYl4rxaNHhyWqDe4stiBhXXYsX29YzjTomW4uzfRGaGOj7VMf8ARDG+5UMIy4fdejLcvvaEJ5tCWedU+hHDfB4SXi8MHi0MFj4/nGvMXh9qWL6ZUWM1psoPtg0eNFRNBeEmhH8u+Mx5nS4fSrCoAYIonnh5ailT0Q+DUzBrUpYtjcN1LnTmgxNDBIan87QshxE/X4/0AOAMS7d6JVfoOSydylIH11dQjcW280SvXGNfbHocXi/w+KBy9589UGD88slDhdbPH79HAePLWhRdNTcczK/SRvspM5WELLPhcx6NUe62kAYBsiwgp5aEmzHML2LNYcF+L0Pgd/+BrZfeYHnX23x8fMjbM09LLTxg5PLN3X/YA1mwrunJ3i2bXCgXc6FeC0R1unqM2RB4r4RpLuLBUi20pEBbk/kIAFBXo7L4hf+gAAaLZbaAQJFwH10eLx06wJsrMLaaJfkhhitcoGuhglbq3DeK1waFxlPAL7jkPDd2/u47O97m67bnhJaeQU+Uvc5s506W5PvI3J5Ngjuc6EYDxcdjr1PDRHjqE0swKZvYCzheLXBa48ucfCkB2lXIX1fgxrCsu/Qn/t31V8PEOwtE+cQkpNhaHpljoJYzIhCGpNabo7wjFea8eYB8GCh8aLTWBtXl+db4OMNIbzdVgFHjcZx69ZTufS5BJ5vGeedBSEkPUvgdKy/y/sIx47dp6t/+M2zzlsbxtPtFS55i3u0xHHT4KBROGoc07Qxrqx7rcJx6wDyYePyJhw3BgoKK8VYq+TMyMiZgTH3k1p0QQkIqHb8HWYGanLnmYLbFEmT1RiBMjSxlOSEktLlKnC2HQCM9VS9H9BPO42Pt84/4PjiCPfaHgtlsGgMWmV9TnhCv+6hWgtSjCdPzrE4Nli81YBWbXIl3tVCBWwPeerBiKvlhUV6Qua4SdQo3PZJE2hB0AvC4dLRuua0x/aXP0R/Qbh4vsDZxQqdVSBi/ww0ep+T/+rsnrOZ9hoNpYyGNT+OMf8OKXMdD10uiaF5Ygik0tfqM7EpLW1tNhkp6QIghKRJ2juqah8m2CiLRdM7c4xvly5CoXE+Fp5t6L3/wYnRWHuAudLO8axRjN669NuNX23PrbzH8bqhDvKWabB9eMNZLoECDAeGKRwXWCYiQLcWy/sGzWsK6BlX3yRcnrbxuOBroRvXzrcvVLyGvujd8s0WQMjy51dXTBW4vQEt5inYA0So4A8wp/wieqYmmZ+TLzt8tsTQmnCoLR4uKA4Bbq0RMUP3ivGsS8mAFAGHmqBJxQimUvFFu39RxblPeIpdcPeWS7jeF+gArTqI1H9nk2Okq7tzcF4p57zdepZgbRU2HhgHXyQJDBip7+6ax0uHQsk0yHqW3z9l2OCVl0+MKQBy+7PLMzAEBINwleCMWIAAOfgGsaxjWa2yOPQZRJbKJecI9tNFY9A2xmd5s37A9LS6UW51uU3vnLyKBVGqYgWFbYNiURmt7cLknBkhzpx3zLCJ/PKsjXGruLUW1Fisz1usNy10SOGsOM4ImQldiIJgF2rVWYVWWbRA5sMgQ0LHHT7DTOR6XZNn5j6YS62NHUc+PDV8dwtnMbQCyFMRihjLtsdq2WGxMNDeNBQWD+o6N5vmCA5c/Y11rMLBose9e2us7vVu6WHNUAuANEANOcXakHOmFCOdzLYoOdHM6XKMgYovRaXjJa8qZdWCjpbAtoe6/xyL9zew6+ToVy6JHO6zXxNUsSLgy5YyZXUpc1tbNex1D1FAlodSJudxJgJvbvLpogHERF2GnYPiea9wZVz4sctkyD4Cya85IUwGk/cyow9cK+MjuSiF+63Bg7ZDQ4zzvsHap9EOjBPgFHwv/KF6S7jsyQGDmLnVAYIAhKbqXWNGas27/Py0AQHHqvwBU1CVmpkgSFi0Jf3m2MDlLKxGcYfIBkWMhe/Ih9qgURZLbdBoi1ZZMBO2vYtfl4rQWEJnNDa9dj4Majjjk0o8Kfb0u3QydNkCh/sYu5VsiNJoFKMlt4a7s4e7nPhbo2P9MwpPPF+G801oxOBbZo+UgKDcV9ZHXqMUOXu6kURntHlmBVKcOSGGe2Fyy+IyE8j7aBBxXCZXLw2ahVOEpBm6cwsFGaOistKK43ciOP+OQwt9AKgVAY0CtQq0UG6BpYYA7ZV37Q8AlErcKQQwUEK1TY2ag9WLANh8ZGbLoKMW7WOG3RjEZBnhufaIqy3CIobSjr2L67AEpJA4fQu3kiUAnojoKUX2dWCo7MrmMdeEMOYca+H6K1uXMyA4IbrU4oTG9yUNP8gDfpyxLh0wOVPOUgCIdH836xhzEj2NCXnFv9LW5yYBDpse2ujkbOnZ1Y0J31MGxJ6RfQ9gIACCcaA+3CaVfulPMAUIkgniDvsU/IH5IJfSGS0qeCQKtDYIyO0yhHHgCAeCVhYLbdIMmiwOFj0aHVLPErpeozM6zg4ihewV7brX6FiBwNEG7c5NHVMORjILYAYI/D4LCHCA7NhwvnxG+TMLqZU50v6BkA+OgiFXQ5iMtsqmwYtsfA7SfJOeWc2hcFgHWd9SpkBAWJRmauCoyXVX1CPFUD4ePbAEzACxX39e2SrD5Hw3LDQUdGMcTa+9o2cDhMWUqAl/hUK37BRxoN/tyExHEWCdOSMu/hTKscFMpXLtNnjAJt9u/TLQxroyLLvVHpmBhXJYI1zL+mWjDQO9Bwc9gwziIkiZH8EIGNjlFDiVlGiKdRtjDsZW36xJeHTBhLCPXVqBYfx1GI5i38I51nXszAphDZR4jgfTKw/aQyrx4NBaMqE3tZNfF3TLnCSGVZycdFZhbRUue4W1dSvGhqWcZSIk9teOQADj/XqsbexS+lQ59q4k/Jkj1xzWPnH5RJmCsNTuYFAuAEGYEcjt2ivo0i6bymDhvW3irHC56KH8YBNWjtOKMyVPXpGTtwtbG8LAHFVmgSysUM70Qy0kAACQgQDJEEBsH0hpF4fLP26KmX7wXO5ssm8GAGGhAOWiKyypATNTgoPBwjRI5U39rokMFZ3TmW+6nG5cijvm22dAORCgffIdyySYe47ncK9gyGYKLLICARAsEhhw+0W9e4CtdWsHdABp67wNGyvYAaTvRHX6X/m3U/OuUhOzogAAevfJnUmMgQXYb5cjESlywECRW0FSBQ3KoCYBgzlJiKr7RTRBfF5eS8tFquZEGeyjPG9qQgh5RxjuFkJV40JJ7Kjfnlx0ibyWy/lBaBUjxPCE8Mc2goN0T9dJDCaltp5MkDH2MUwlwuSnY+cjEHwFrowzg6wtsDEU11sIyj+/Ru5gKNvKLlMBkPsPBJZgDBCM/b6L4pilu4FwPhlHQ6SxSYbkKKrMTCuAYAgiQhnFQ85MC26tA6JEASu4WGzL1ttTfQf2Doesyc8wEt3PPs9AWAkxKH9gCACAnAkYAwK1UKI64s7TlhoOazBwVk58dpQGR3ldgLKOFWo1NpjO6YQB4LlrjZ8QnoMECoOogVjZYTn7ZLpzg5JbEZCtu6ZsIqEthHBRQA3MDwDcbJ/JKUnlynTb/Z9lsLcC8JYdc9C4GTh1foRrlDiHkAOBcJ2SNxVM2sRL4DBF8yCAewcOuI8P2Dm51jnydMwcUHZNE0LJFhDBL2BV0xxp+64VEOdISbSMPcqSgSCkVVkBoPeEb5xVMwZakkAxzXlcA0ExWuUWpgosn2Md89DfOWB8H6m9bjlGhYnN1vsHrI3C1tYBgRFNqabY5fMd+oHV60c79pfOhTxx7N0Txp1f++C2pGqfrhwjO0jZyMZMBlPXS6t6pfTDU3Y58nWQM3EgKXwrAEFpRqgBgn2lNjtjP2sJzkUpK2JOjZK/75CpkDCk6MrOLenE60gJBOIz2dP2GRXHRHKiKcnYgliOYww0Cn8D4b3vEh1RLfUF0CtYHzFCCqA+sQTkZ9ghZBQKIMMgDyCo8Yl/Os9aiCkRQ/oQxBvIP4H67/BMpPNGAATBJBAiW+IzDeeMPLwQBVP4b8gMhOnYPcDBxEqJAYSVpoKa6SBTEjPYi30lz+bJMV+BN+rEa9oChA9mzwhKNK3O2VlCpyh68W99zH9SjMX9F2XepG9mdfP9MZg0O6vQMUXfgS6woZFBzCcUY81yVwhkKeVhJUtQAoLweTfU6Dz5A6agkDKG1c3FOQMAcpZQYwkC1R0+JxWQ76QhJ0HN4z94mLsMeC4THoO8zS2tD7DbKdAj5mgeCR2K4yzDbQssRpgV5QAijKM1cECUg6UaIFDIB5PSX2GuI9a+Md/l9aQZISxaNVdSLof558gICQoObaESFeUTfFimvN9TqKM3SxkPEDz9Dm+eIE+3sHWAwDnVeaUnKXpVjKKVaZNkBxjI0XMWOcA5G2ARnQYR7l0o/OEDQ3GsfO4YVei7RC6vC+U4N1d3HpgQqnKLYY9A8ivY1wExAAMNwFJKUR5WAJTMoBL3zAD6wDJw8NAn4VvgGcxMAcoxT9Rlj/ucmiyV/kx9cCD0deuFOVERxTUIg6LOxifx/bo2/9JUMLiX/Yq7M+K64u2DAiL60wB+FoAG8PPM/NeL/X8FwL8Cty7YhwD+ZWZ+d6rMTzgkcfdDkYovNGg3hnmv8hnhfJlT4ICqpuJYn3WQVcyEJ48LPhCx5kwA+WVCRZmxkwTzhzg32t8i3S5YEVEX7c8BjXe2YJeUAwzBrdQoTSc5MTot7jnn61NcVzh+Uv65h5KfclJzZdX3Z/XWw4MCEAjlOMVdQWVA3J6OnVPzCYngwYOK8IKEdgksQHJgdB9cG5kFIJCNpQYIks9EqotkEzLnwiJ0MTtvRIbM3pg57HozP3ULU8Z9lJdUiAoMS2554Igz/UEBmyny+9j9Ge930JHLfaAoZQGU5lHI8sL3EcAwJVOAR7KYPbv30sewQvfZc2AOw+Qm3b8eeWclKJrzfDPQQxXrWXFsrUy6y0sn37KrIRFpAH8bwJ8C8B6AXyGiX2TmL4vDfh3ADzLzJRH9awD+IwA/MlXuJx6SuEvCrDjQ2xIYGB6i4jJMkMXMPoYLTqjHcL6RNHOIemACyEZlHhRxuFYsozI7t362IddrkHGq5ew5dYBh+VKkF3MwIUiWQFJv5Tk1GfMrmM8qUDRP72IE8rUMkG5ahCHOkX3S4cqQwgACpKLP2xNXkyClspL5IJUJbybwBykabgPiQ6axEfCWxdnwAdi8HtkzloBg4h1wUUZ5nVHxbEFWpipayQg7kGL7r4cGyigEKXPNfGOKqRYKmW0PfYcRU1KnRdTS73idEbYgTSPmSW4eTN85q6MDApLx4OKYnDGpAwAJIBjDY8fqVt5Pfbzaeat3UF7K2gd/AsA7zPy7AEBEvwDgzwKIoICZ/3dx/C8D+Bd3FfpSQMHgxVM+2NYiB2THkMAACIAgP38sZ4BLGlKn/KdsktKmFhSzJwWiws3DD4dAwJ2TK3X25zkqMtRjOPUZmHBHKluapKkABHMG0USr1v0K5vobjA0AiWWZPs7t9OfcEERTMbMvt0s/AOdwWFd0Vec8SKU/BASZWEYgzlmeV2MKgJwtADLGIJSXV1DUUfoQFM9v1HTw/7V3bbG6JFX5W/3vfTgMc2EEhcmAcnFiSDTRSCQkJiQGIyYEQkLCJSYG8UnwxfCiRiXzosYnEy9xQDQqaiKGZGIwo0EfxBcZTTQiYIBwGQRhYJyBmTOz9/67fOiu7lVVq6pWVXfv/e+965uc2X9X161vtb5aa9WqqV5PQ4CEhqaCEDhe6dY50x8ZOCGwz6dippyDRFb5LYyNC6oVNJH0yTkY43joCdbR7WUEIwo2X75pB+QNKU7oZJbG/aAGIhDeG64p4H2WSAMnBs71JZDK42sTtNuxHzqGz7H4Yp5PRA+z4weMMQ+w43sBfIkdPwLgVYn63gngb3ONbqYpCDxWyYAvubPLw2LCh7/U4aYgjBAw9bQ1BQBwBPhcKv1QYjNdIjNqDQZObTdosvm5icC2vaPZc9n+7sZj693Gx2BuihjqjPdzXrqJqf3Ut8iFfEwDwE0I0+oEpbbAb8uW5X8dLUGifFwwCasE2DnHJACI6n+HDLAyU9vGE4CCc51kTpg0A7F7xXXMHVwzgX8z/Cke758/JWWEwPA0jxAE99RzLFT7EAj3wUcyRkEH0a9gDSdCSeOVEupcUA7H8vs5TDJC8iBqEDLaPWDeYpj3gRMFAEGURQ38liVSMPSRk3WXvNu+cBMCLyOZF3iaX1eqj9zBkFjaVUZfbj541BjzyjXaJqKfAvBKAK/J5T1fn4LEuZiKP2aLdmbnhr/g7g55U9tsqdGcZuuK170U/vaozj4QvM3xb26FhWt/HIWLkM+/Wn/2z9Wq2nXeuZlTqdMR3ybZTR9Vzp6wkohBzEdgmslDJgOBar9nA5vtj+eXMGsL5OPhN8kaBAGT1sBJlPNNYGaXQEMQWU0QaBG4U2EC7j2SNTFS3hQx4HWtBd+JWYJ/3hWaM5Hl2i1pTKrYG5ksAAAgAElEQVRRaPnff4oH2jaKBWSKAMV5pgjJNMDvjZ+PaxVi5CB3OdKyR8P+Xn4YGFrXpwDAlwG8mB2/aExzQESvBfDLAF5jjHkmV+mmpKBWtEpOgi5L56x+/qB9YuDUyXrDZ8JB25690Y1FQE4evw4poJF07EPzqkjLOIG070BIg/SwJCKnLTCCuSZ5PVygCX9z4MSArx6I+QtEyUDE5p+0n0s+AmyaI5KGhEt5KhaBRUAIxDyITF2FfPb39FKy70IMWpHWDqScEG1sAu5XkI5oGK+rFvwWSEQAYN+6d2zLrD6Ue7DNuSS+rA7/VdI5/o3LeRH6BUimAduOpu7Ym60hO1dRYVBpPsjh4wDuI6KXYiADbwXwdp6BiH4IwB8AeJ0x5muaSg/Q0XAmBP4WubEQwTFBzuE6KZL4crqDwUwGpv4IDkouQQnTlxKCGBkAdB8PJwaSr4DGfyA2CIh+G1IfVtS8AB4ZIJcQcOdBkRB0kGf4U92Ztn0dKML6uNPhnNdvp5IQxLQEgKsl8LUFPM9UZ6IPCTKgEd5B8KKYX0GkjaA7GLqrMTNotAKAPIPmDnmxOqJ9zC2TRvp7qxEZ87edzytl8YnBkJb65sv7GLYZP3cVCYFFhfkgCWPMGRG9G8BDGCzQHzDGfIKI7gfwsDHmQQC/BeB2AH81Rvz9ojHmDal6NycFNQ9Z8g3QEoHkbTdziNKUAwsnAlPRCCEZzrl9j6VJbQA61f2awU5qfAXOCxKBcJepyulBmRQhEGb5E3Lu06z++ZyUFicEOUS1AwIh8JcgckIQFWQVZEA1i+dOlQrENkEK8mnKRZqWfAZi6vRAu+i9a+IyS68OLTFYAtcMuBwSMdBgCUEQuPWVJgSAWX1JIgAYYz4C4CNe2q+y368trfOgNAViDAHPR8A3DcQ2KfIxOdop+rGEDKTSnf4o+gGsQwaWmBEkrOlzkYQQGc8Ozr4fgbiyIEIIRDKAeFogEINZf74OH7FVDhNiZGA8JzkWBoSgJAiRH+QpRwgqVf2kcQDYEBpCkFrCbLuvVaNvAY1D41owEMhS0B9dXVJ0xKtNBGYMn+MFvvgFOChSAMxaAp8QpEwEOULgp+fYfM5nYMhTll4CzWxprQFAW0/8vsqaFV2lw59UFEPRuZATgimfghBwOJEEFX3VmhpEu5RcXsznHIdkYDq25zKEYMkywxwZioIJfsmvQOrL0iWpKfT+2OGfVxKCGDQz7C35AzcPSkjGKRHGLtnXalsSJPX8uhCGQ8MmpKD03dHanFOmghKBJGkCYm2F+eNtLiUEsY+XFHmWIDbW566B37uSUMal4MJf2rxIHcyoxIdA42zo5M90QrIXSYKw59fq5gu0A+M5iRDkhKzjUCmsBpDMI6WQ/ApSoY41e5vEoOVdQbmCJq2WqlRboG2i1rnQL+a/iTVjUTQGSeZ8DKXmSv/eXgWCsLZPwVY4OE0Bh68lAJS+AxmkgpnMx3qBX+I/AMiD15bagbCeqmrODSmBltqvIBWDQI21yYCElcjAdE4gBNI91DpRrkEIVOsEedsrrDxIORgugeZSuLZA41dgsWT27cW8msCr1PQiZkax2oFUF9cmB4c+NtXDNFKghRtYxzUduPnWaS9Xb0rg++dT54ALNZ2KcFcuxG+oFBrWptdgyQwwteOetOLAaTcZGGPwtkwtQczWs3QE816+FBkAZlOBn1dDCGyZIuFbKKg1ZgAi49y3WP4aNX4KkukgN6Zw50JLDFLagpgZIZauHdP8fLmVUzGCkOsTDziWNLFspCWI5bsKPMEAmzgaboHNSYHzEYJpTse17bsCgbGGloAjZiIA8gI/pR1Yo39rfQhr1LOqaaDGK1/atKgmAI5Gx1yih9Ys41CMoCoyMJ6X8kqEwH4rWUJWsPGTfy7YaAkJYhAhd1vEJlgCu4VyChozQom2QEIyGmOGJMQIAkeMHPm/eb4aai+17yddBaGfh0FfHKfyYnDumoIeCPa5Oi9v9rj/QJ2pQDqvWt2Aeq1sCSRfBC1rX4sILJlMi8vAghgF5fVOqxdi2gLvASVn2BUqLCnscHBOoR2Yz8uEIIXsfcutsNDUYfONz7HUoXBLPxVA5xMgaQtSdcXMCBpnRIvSVypFAkrq4tqUrTUD/g6JGlSbBA8ETVPgwYl9z+L/u3k2alvhQzDn5XnWJwRaGGzPoJc6LfYmLYBS/c/NSEuER2qDoxhWJQa5tmLXkiMDLE8QmwAQnQrXDhS1FKXOhlshFqFPzOtpCzRmhLWQE8C5cUGjJUiViWkGjJDXtqGNeyKFMr4uMDBtSaIWfM05MPsTcCfDLZfClC4z9M8B6wwKusiCut0LgW1WLOQ+/m7c9KrDeuu4pU2LpPS5E5lGJTVNATEA8uQgSWi8c+4eBWntgD2WQhQP6e5NCXYjzSxDXF2VH5GYtl9rExi/Oc1GS2u8p74ZoUQrwBFbCqhJk+5kyXX55oQYCZCwhnOg2u/gXHSs26CZDxikj9/AOhOaVTQEMRueVkuwNiFIbRVty+Ve77W1BXPksPgNjwl+7oS0NEzpkmvSRNmTCIM00/cJqZhPeFDFa+qF/FEywPJrCEHKsTCG1FLEGPz7EoQwzrXXA8bzK7BboPtboQPx2SvHlnGQSrQFyXqUvgVaQhCDn1f7jcV8C/jxGlOKWuJwkUGi1sU2EQ23wKakICZQ+czVX31wyNAQgiWzH0kT4BODVLTGrSANFmsuHZLMBVoTQtXKBibofTMCoCMGqjY8hL4Ec99ThCDY1VAgBDktwRpYY1VDiWloLWEQCHi4Y9N5aQtKNAhLL720vIaEnRdiu9deZgx+Gk1TEIXvbLgWGZBYOcHdhCW14sBPu6zv5KFQq45MUivhw58xWgEizVB9j/dVkCMGlaglA/xYIgRzHSs4Fi4Ev1+O4PejG3rnc+Rl3W3M51uvIQZraQsOFanwxTVLOGNYoiVI7VFzudA0BcFsUhoXewPsfPXtwYg0F7V+BLFBr2SMXmJG2FqLYEFeL/3jIH+n8yb2hZlDBnjchZqZvK8tAAJiUFN33LFQIAO2LygnBGuHBnaIUIV2JLftdOBwmOh/oGVMtLvWbdAQg7p6ly1P9LHFDHoN4W+xRIsYK3sVghqZS+JTsMn8wdmq1JDz0Q6Og+QcWyJwqISgFjUq3LqQpPFVEmuGOLXgH6gYNMhLcz7ozjWRrDqDrYldMMIkRkWN3d7mEbUC9p/Y1lxeOs4RAnlXyZWkRoW0FYmU89s4GgWxDiObE3Mz8tLuSl9GTvhwItt5aZOrxuS7sz7WJgQGcUJQ09RVEN7bYIhoWPLfReFczQd+uGJuQ4+p+EuQczTULEPU1Lv14ypZZbAmrNPX0Af5A3diD0kzq3EFgj1fOkhITmdcyPE+AumZ6dCJsvZTKJqZCy+bRAacemOEINKPGv+VJRoVLUSNQTeq7PdzPzT3s4eg5h41OzXf4dVxXKtD6tI1k4FYnkYG0hjI1zU3HwCy/d43GcRestIZrnblgbb+ix43fGKQMyFsQSRqgpZYG2DWHthZ1jHPHDV2ZokQuCaFsj5HUetNmSMDQJIQyHWGfgQxFId79iAKdO4fUAD/uQKYdk3Mbet8nQX3WrBjxpq38iKF/+VdjAgABua6Oxra+PkD058JgT3Xjw6Aw9g7v7axHQxTIY41hEAzyKiWDiXOlczcahzaS4hBKu/0bMb8vC/+TFwDf2lYB8StZx155oT5Z4lnekAIupAsSPmldldB5AVTEQKEaWs5r8X3bahooOalZX0wtg4/PbaqAXXakKn8ARKLJUK6Ju6AtshFkDAD2a/sqqJtiOTBCulB8BrsaH4hUkI9HQNcfptKNj0qNR2I7VUOXLkxNrZE0ULb6iT8lZPfFDEomUATTLEMcTzTNevfAfjx+yfEbNcSadhgYEoRAlV6IXLBilbx3/DZXyGkmAU8yuE0gfC25bb3sgehU4g6x69pBdOkFqn9EM4LB8iFslBHRby0fmfmmjsaAmNULF84U/DbOhr6/wCmWRC0BFsSglh6aVwCDVnoI/Vq4DsJAWWEx++HU3ei7zyQkV/Wlkq9WIR5d8MpzftdSwiyvgRaQrBQT3res1Rt9MJVEXl5YyQsTGfaHSnAl+ekLK0C8O+zMfp7L2ULxguvzdLnOn0PK5j2zlt1v2VzzTx0uNh+l0QvVPFAGIbdEY0ZzAilm3iE3vby67tkF0Q/rTZQkVYdHzPbanwFfFOBVMbXFvgmBNsH3n4qKqNP2Dg6MtibwUwQXLv3sKkD7A56MfOBVgWeNRucEyFYA5r17zwi4JSmMp1k3qeCYEQA0iovTtLGfDSWSW2SZMz8jfbGm+2D0BkzPbvUfSrREhRvRFSWPQDXJviahbV9AUr6wmHvXq4vcVNlHjmz6AF8josxmMKa+SAZHMOw1z66KkAQyhpCkNIO+P2Qzq+NEjt9pdk2+mHlPjh1/RW+BjWQAhaJ8HZLdM/NdflpTlsbEoKioDbKeL383khOmTXmAukeVGkZNC+u8jqdahP3ML9EMU4IwjEgXV7T3nlgS7NEqu7ar2ItcnP5eYFpPgV8jPBn7HbG2hPQCcImiIPOyjr5NiAE2he41I9Avb89wvFVu7KAE4BUmRJtgX8NWmJAZNDZa4ZxnktnTQfe+nVuYw4gzG7FfRAUhCC+mdKCoWel0doV+KMgErz1pWBA0m+3j+cj1SZSZ58Fm9FrnEkHzQD3L0Dw/iTLZ0yIF00IYoJyLW3BEkG8tS+E9Oguv8BXwKCtPrDgAj1Y7z8Ko6T9OpKuWV1w0RoCCVpyIBEDCam9Eqb7O6aliYKOGJQiqMcf0buE8EsIsfMkBJKQ1S0P9IRJxUx5LhuaWFSmAls20r+LgrSFsru9+nCruJMhh3U45H8l1BACDUoeoxXSUmTDGiGs2RtgKTHI1V8CqS/BMMC/53WaPTBc8zDHg/0kmNxM53pDIAA7mm+TIwC9MrFlisM5uQ81hOA8tYMacqARzkvjE1htQU3btejG/5UIMCCj6hbrypcf8sknUmp0ccZbOMJH9wiQtAW5tqX+Ft5LsY4SZF4au/eBvdbpGHHNmx+DnzBruiwRWJMQaJwZQ6dcsfmsOt4I+Uq0BTnhza+kZoRwwtQvHBw1gl4iCldl74PmU+ChNyR6yjt+BYmybn5+Lp1/LpfPc1EoMSvE4O8+qdEWSEuAfIIhjfEaEwKN5fqCa4rZ4FXCS1KdawhBoXZAypP7zrfQFkhtu/ejTjNwLisWlOiBKeqpuzwxL2A02xDXEoJapLQFPiRiAKTJQU5oL7XtlzqDa0BCvf6dWcsn4eJxeZYkbuhTQNh5tmRAEDyeQONpHFsTgot+8WLkoGTGrg1eVII1NQaSLLRLE01fKbQ2IASLcE7aAp4nRQhqNp1aE6n2tXsZ8K0jOrLaRt2t1poLtJseabUEKVhiENMWSMfAMq2BLW8Ry6YVwrHPpy01jON6awqsyk8gBF4W8ZxbVR0ZKGsjD3+CJ8XoP1TktAUxE8JUHnpiYJcj8mMfRGNo4043BMUd5/J5agjBIc2YA3h+F0sIwXlcp7MDZQGkrXttukYASqe02oGpjozZQIPUds1+mkQMgHKtwdRfJUFInSuV8Wv4I9gxYy+Mr2QO+eNM43qTAg/WjwAYP6wxTsHwgcsCaU0yEMu7lNRuQQwk9XzqVUrFNajRFmy1GdN0nzr5oy4WUDFCwNPPU0NQgsIliEBcWzDXOZ/cxF9gZcRCPEvXaDA7FvI0rfBaSgZK8mmHfW5G8IkBoNMaAOnrXsMnYKlfQm1b4vkD+HRrYa77kkTj/I45AQ3EIJVnyBeW07SbK5N7waUy0lh+XsQgBtFRUyAGvrYgaJNpC8TgR9BrCwA4uyUG59yM4+w3V6Gm0fnnkgGkOHhPKXIsr3TsyBCCJYRLxNKxLbHng5MtmKmPKw0mDZeiKaGOFGKEYK3h3CUAMjEYzuWJgS0H5O/FGksNtW3lkNt91W/vquDaawp6A2eEGtj+OGOkWfjYVyzHamtV/2sRghQOwZTgk4PcjF9yOMwRA4tVAhn5cQp4utPRSL4RWwnv3P4L0e976ci7IiFQ3Zua+1fpLClqAfq0s2aMGGjz+2W1/XLLlZfRopQYAPXkYK2lhtq2atq5iC3jzwWm7ZLoIIhPgGFcsWYFK5xyZbWvS402IVcuh7WJQa3gdYNGyb4EMd8CICQGwPyh8rrXula7z0H0SiuFftSWzT3WNPWUCL/CbZNz9fNz+ZDH3nHqvq1BpCRisLBefo3+5frPU+toCMhkQCPUozFSCrQJpYIxRwxiabHyEi5ysyYJqU/xai1LvOaaAgv+8g17HQDd+JcwxirICJjQ2ahcIJWYFg4JWuHLCUQtMQBmx0MAotYgCKrk9aP2fkYDBMU2hWB5prJe55JObtJIWwsNGQDyhKCXzzl1KYI75VZprAZ+33Ptj3CumV1LEItBKjs+T25GSKGGDOSG7S3MC/4yxaXE4BCwZt8Oe4TWwlxv84H/EK1GQIJ9d1IOLbWCZg2twEU+xpLZuL+ksZQYAMhqDaLEwMw7W/pIejl3BAMzzzqTbCNSiR/hzyMRfBBP+hmsNIotJQP++SghYBCjO3IsML+oxjEF4XC0AMyhcPidfs9rtQFS224ZHdIbLiXObSi0l9S9Rr/SY2u+fd9cfAj+v1vCoPkUiJhimBPmADfqOAUr9UEhaGse3UX7FFhws0OOGADzfddoDXxi4KNqsPFDFFtC0o0zprGhaAQ/RgJEcsDq1tqBc06Kqno0znRaMgCEhEDaCyKIaCh3rcQPw8+bDdYkxIpQ2997N1CRFRRchRxoCwr9BDTf9lJtQvlSyeWkKFV/bV2l9WuCRqUUd4P2w49js/2qh/PC9TYfOCzQRiXjaUOmYam6cYSUxVraghyWPqbzIgMlzvk+MbDnOTEAdFqD4fesNQj8DBLXr3UamoQwmy5QP/azA8yZvS63jBPRj9XHWw0IQtDJMKnYiUzpFxDLX0QGANG5MEcINLEeAsSIGEPOITNaX08u4etdMsAJAS+ea0a7TLD0GS8hAkCZ+dJRDnkFNYJXbkeRKYIarUCszDzOzCaQnXcL7D25KmRgwDU3H3AYgIUsnWeevTHYI9QScPAPptgpu7JcDBepCdBcgz9gxswJXKjntAY2TQpylAo/bW2kZvzdkRl2TRw65AaB6gAzjvxcsDgBlTqg5467/XiGz5YVBIGD1n5BkJlFZ0wItWQASMRpUGoOoojl5/c6R0as5sd2Pbhu792xq5HGfwT3eQ4+SbO2IAX/cSxxLgzyKerKz9pzE6F0e1uRgRptQ5z0uMd2Kan/jfIJyGHoXNdHIwWYHzT/8OeHT+itOMmYEOytLHG6S93+Q1H1rw3JJ69Wa+CnaaIfDrJ9jmhoB3Q+85MQbKMM62xFw0jeAWTt0KOwND1APCa+giBwbDYLKdAalBIBoJAMRAR0Lab+KsjFkvDGcx1uxtroiFKbCxeVhPWr6kn7VUntbUkG1FadAm2AvJx8+Ctt/MT/2rxr79LYUIZNSQFngD3CGSUwahDGWSSfnfoeuVaQa9Tohyj0NcsLY/0uXRYuaQ2k9sUgRYnARzGHoMFdkKn+MRO8wTw0aAp23VDBdJ0TYzChWno8PV3MeBN4b13zATllrTA1+0hAnzBpNeQmBLIzIuuR5C8gkYEMEXCue6knV29WiyjHd0cksu+EkUmRbX78a7NZbYFY96xgkn1f/OMC4bNE+OfqyJkKambw2rY1dWhIQDJOhKA1kAiB6+x8VWB15oePzUhB2g41zQMBGpcoWiEC96MqvY1S/ouM7loSa8DmlcjBBtpuB75KTwPuV2C1B7vpA7cED9h1AylI7a8wZR4xrksY1ROjtgCYhIe/S2DoxEeToAxMDYiThbVQ7JegJQLA7JQppUWIwJIgT6ZHnFRIUiAViSzFcBNbPYMV29l+jdmDGA0JYhD6NoXQz6DzL1B6HNSna4mEtu3asjXERQoN4qf5hKA3bELJKjV0OQRrANPMBxOk7Xl92NgFAMTtdrVaArH9ijISFkfwW9AWJwk118Prc+QISydF2mDvM24aAFh/ATATxfhhEwatz64bfQuCTdPzAotoyGcwmhE6JTHgYCRhyu/0Y6Pna2e/yvqTQYgSmgGJDCzyKYj4DQT3uFQDkVu9wKqz771/5yZtATvm5WNbcI/cUnY8THV5ofB36ypLLyUDa5OHlDkgVl5zjf5GUTFzAm9jrSiSFwEDXPPVByOsTZlYHHzNfuIcpaaA1Li3lmA/L82DfYWkfsfui5Q3kA3MoVCTZj9QK+BtnbatHS8zhrG2XsXWyZDIzLMCfnH9LGiymoQEYhvsSMciCRFU16uGUNYEHaokBA5iQrrGyTCy+iDUyqSfmxk1p9NqAy/78H4w7aABzow1OQ7HxoTCQlqNUOM7IM9u60wAEmpt41phW0IEajQX0rlc31LtEEITgaQpkOrtL61Boa0+AGAdzAyOxhDA/aik7piAAGT7ty/0SpbkxYT/RZoRAHcWnsKkepfOjX9TBEeWFXENQI4IOOmsbV/+EMzA/fqBGNj83UgmCAamN+GWqH4sAus/MAa2iHquR16KHJHkIchT99Gvv4YkJAmIb9IwCZOGJAHFtHm0DaI9QsgvtRNBigxEZ3HOskMMq0gSBIxvm2v/9ZhXrNg4J/MKhBn80ng675tTj9TdyhDpYT31eWoF7prag7X7lmvHX5ZoIr8vy06DMtreB+hgsKPhX08GZ/7yIwrHqNI9BFJkILo0+8A33UgFctLKJf8aU1qBHBGw7XIyIM3qrTMiWT8R77wBAfv9MPOT1P7TNGE+9meZayP1rgVmnEQfUhsn1WocYmWl0M4OoYgRA5u/oH0REhnIaWomkkdO+rT3xVjnrhtO7g1w2hNO++G3DWJDc3UOp/IJAu9bzI/AETYLAqZpNQFLnfg0eWL5Uu3Xx1hIlyuBpC3gbU8r2S6JYA3RNAWj2QA4otkbvaNZlHCh4g++EjHwtZkSmYidAw6fCMSQi/QYy2tRYyJw0m09TLsjkYYUeowBrAyAk7NZU2C3TQYmAkAFI83kSBgIvu2etUbAxwMFyf1SbXcciz+g6E8NMakhBKlATTy0scWwFNVMPiM0OqX2MNibQVtwZmj6DQzv3N5gclKeO5PtbpQErCn0lqwA0Ar4pUsENedi9eUQeyT+K66JR8BNCOPc4JLjclzBZnsfWC3AjgyOOgPT282Phjz2fEx9K3nip4iAfz4lsApdoy4M/hVohLB0bUvJQCqf287QawOawtL2GJ5h3xNO+w7myZNhgO8xOB12BBrfDyCjQh9XIMA6G9o+dnBU0qLdewGKBGrCoTB2Xdr9CqI+BtKIm6s/g9w9zBKChAOo7Q/tzBSjgsb0btQUWO9zO4N0ykf6VLtCx9YXfm/xMjGBqikT7b9wQmpnR3rfAK6Wz11fKQmqGUcdszHNsoK8NGDo+964aeR4MF0mmHUHpQ2xCSnYEXBMBsedwXHX40a3B6Eb1rt3rnnTF1jBe6lQg2vOzekFF3LJwbUxvikAkH0DAnIQIQOSgLNOpPve4BQd9maISbDvCc+cHuGpsyOcfuHWMODvYVljYAz2n5y1RweOapF17Wt/e2WhfGV/AWDut3/vxPoFH4DY3g7G65evbUkuKfQhSIbY9s+z2cc7lsqyoFPASDoM+2bH536273DbUYfbjgzuON7jZk84pm4yPU7hjx1No3wpri+B68jI37JavdIShVQuGmPt3g21mzdNeTZWqPq+ZETu78H/aO7LftQU7WjYdO055rZtO7ghTPWbFgcRvQ7Ab2Pw936/MeY3vPPPAvAnAH4YwDcAvMUY8/lUnZuQguOOcGMkBDd3ezzn+Az7nnDc9TjpdzjrZ1Xg8DdelyTYYn4EknCTcFlNCZL5IHctsXviOB6SlGbvdUgCpHw2fSAFHXb7HU72HU77DnvT4dbpMZ44O8I3PvtsHNslhTd3IOtM2PXoT9iHIwgaO3udlp2ltg5OBMLJonJ5YiruQZZQ5DQDibxiu7XsVwgnF1vm54Obc4KybBnp0A4LhjQSvu5o0Crdc9sO9948w8vu+DYA4MnT4zDcQcSvxcccVt1Lj5ji1uSTRausFPNuve+Crt3zGAXjWpFw0tGNS5t3nZkCou17wqnp8NjJEZ443eHLT96Ffz6Hfm+DdWcrRLQD8LsAfhzAIwA+TkQPGmP+i2V7J4DHjDHfS0RvBfCbAN6SqncTUtCRwd03TnGj63HP7U/irjtvYX/W4elnjvHM6dGgRmYvri+AOCRhFJgNJFv6JRX8a0VjFJcmUjyPL9zn9HiZ6XnY4ERj3v1Zh5OzHZ4+OcZp3w0Op53BHUdn2Pcdnnt8huPnAt0L7wRuHIG+dQvm2yfobp0BJz3M2awuMHwkjHmMeViuKdA/A5X2ICWgI+WDeA7TCV390fIKGP/eRgzz0WfjlfE1CgBGs9F8njqgu/sG7v7KU3jOEXD70R53PvtpdGTwrJPhvRmKuWNAihRPG+vYOCdOhNRosewsPgXt97vGBm8XtW5fE/Qrab51tJbzpMMuX951PY52/RABFcC+J9xzcoxbp0f4+snd+ODXlvX/wrD+A/sRAJ8xxnwOAIjoLwG8EQAnBW8E8N7x94cA/A4RkTHxzmxCCm52Bv9z6wYePdnh0996Np71VYOn9h2e3BOe3gOn4+AwrGN37UrS7mhbaPxzj0frqStlW7Km1xln5bFY3Q8pXynksMbzOfvM+LO0/bF+BXceG9x3+wnuODoDkcEXnrqBTz38PLzsm1/H8XOB/hmD/S3C2dMd9qcd9v0O/d5t2B9sTSokbizG/Ebhr0sIaHKwjDoixtLL6tGDgvvLhxB+H/18UmjyaRUAE9KD46od9Dsc7Xp8xz1P4bHH78RpD3zuyRv4vzsmLMEAAANhSURBVNMX4LHTDk+cEot7wd871/ToY7JcKHfdK/1WtPk12bZW26+BUp5Z4jsxawyA487gZmdw3A1maHvujqM9Hj8p68PhwGxhPrgXwJfY8SMAXhXLY4w5I6LHATwPwKOxSjchBZ956quP/sx/3P+FLepuuEL4x4vuQENDwyXE91x0ByrwEHD2/MIyN4noYXb8gDHmgTU7JWETUmCM+c4t6m1oaGhoaLhsMMa8boNqvwzgxez4RWOalOcRIjoCcBcGh8MoLjrIX0NDQ0NDQ0M5Pg7gPiJ6KRHdAPBWAA96eR4E8NPj7zcD+IeUPwFwDhsiNTQ0NDQ0NKyL0Ufg3QAewrAk8QPGmE8Q0f0AHjbGPAjgDwH8KRF9BsA3MRCHJChDGhoaGhoaGhquCZr5oKGhoaGhoQFAIwUNDWoQ0UuI6FNE9EEi+iQRfYiIkiHWiOiPiejN7Pjb4983EdFHacA9RPTfRPTCTNv/yY7fQ0TvJaKXE9G/sfT7+HFDQ0NDCRopaGgow/cB+D1jzCsAPAHg52oqMcZ8GMBXALwLwPsA/Jox5qsV9XwWwONE9INj0jsA/FFNnxoaGhoaKWhoKMOXjDE20uqfAfjRBXX9PIBfBPCMMeYvFtTzfgDvGMOevgXAny+oq6Gh4RqjkYKGhjIE+zVl8p9h/M6IqANwg517EYbgvy8Yz6nqGXGT/f5rAD8J4PUA/tUYk1yH3NDQ0BBDIwUNDWX4biJ69fj77QA+BgBE9OtE9CYh/+cx7FAGAG8AcDzmPwLwAQBvA/BJAL8wpt9LRB8V6vlfAN9FRM8bdz57vT1hjHkaw7Kk30czHTQ0NCxAIwUNDWX4NIB3EdEnAdyNQRADwA8AkHwC3gfgNUT07wBeDeDJMf2XAPyTMeZjGAjBzxLRKwDcg0Er4MAYcwrgfgD/AuDvAXzKy/JBDFqHv6u/tIaGhuuOFqegoUEJInoJgL8xxny/cO4hY8xPrNDGuwF8cQw8UlLuPQDuMsb8ytI+NDQ0XF80UtDQoESKFFwkiOjDAF4O4MeMMdHdzxoaGhpyaKSgoaGhoaGhAUDzKWhoaGhoaGgY0UhBQ0NDQ0NDA4BGChoaGhoaGhpGNFLQ0NDQ0NDQAKCRgoaGhoaGhoYRjRQ0NDQ0NDQ0AAD+H+aeIvYl4/iXAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "if not os.path.isfile('data-airfoils-test.npz'):\n", + " import urllib.request\n", + " url=\"https://physicsbaseddeeplearning.org/data/data_test.npz\"\n", + " print(\"Downloading test data, this should be fast...\")\n", + " urllib.request.urlretrieve(url, 'data-airfoils-test.npz')\n", + "\n", + "nptfile=np.load('data-airfoils-test.npz')\n", + "print(\"Loaded {}/{} test samples\\n\".format(len(nptfile[\"test_inputs\"]),len(nptfile[\"test_targets\"])))\n", + "\n", + "testdata = DfpDataset(nptfile[\"test_inputs\"],nptfile[\"test_targets\"])\n", + "testLoader = torch.utils.data.DataLoader(testdata, batch_size=1, shuffle=False, drop_last=True) \n", + "\n", + "net.eval()\n", + "L1t_accum = 0.\n", + "for i, validata in enumerate(testLoader, 0):\n", + " inputs_curr, targets_curr = validata\n", + " inputs.data.copy_(inputs_curr.float())\n", + " targets.data.copy_(targets_curr.float())\n", + "\n", + " outputs = net(inputs)\n", + " outputs_curr = outputs.data.cpu().numpy()\n", + "\n", + " lossL1t = criterionL1(outputs, targets)\n", + " L1t_accum += lossL1t.item()\n", + " if i<3: showSbs(targets_curr[0] , outputs_curr[0], title=\"Test sample %d\"%(i))\n", + "\n", + "print(\"\\nAverage test error: {}\".format( L1t_accum/len(testLoader) ))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "By722sgwFbwG" + }, + "source": [ + "The average test error with the default settings should be ca. 0.03. As the inputs are normalized, this means the average error across all three fields is 3% w.r.t. the maxima of each quantity. This is not too bad for new shapes, but clearly leaves room for improvement.\n", + "\n", + "Looking at the visualizations, you'll notice that especially high-pressure peaks and pockets of larger y-velocities are missing in the outputs. This is primarily caused by the small network, which does not have enough resources to reconstruct details.\n", + "\n", + "Nonetheless, we have successfully replaced a fairly sophisticated RANS solver with a very small and fast neural network architecture. It has GPU support \"out-of-the-box\" (via pytorch), is differentiable, and introduces an error of only a few per-cent.\n", + "\n", + "---\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vhH-rUZ-JMTX" + }, + "source": [ + "## Next steps\n", + "\n", + "There are many obvious things to try here (see the suggestions below), e.g. longer training, larger data sets, larger networks etc. \n", + "\n", + "* Experiment with learning rate, dropout, and network size to reduce the error on the test set. How small can you make it with the given training data?\n", + "\n", + "* The setup above uses normalized data. Instead you can recover [the original fields by undoing the normalization](https://github.com/thunil/Deep-Flow-Prediction) to check how well the network does w.r.t. the original quantities.\n", + "\n", + "* As you'll see, it's a bit limited here what you can get out of this dataset, head over to [the main github repo of this project](https://github.com/thunil/Deep-Flow-Prediction) to download larger data sets, or generate own data.\n", + "\n" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "l1train = np.asarray(history_L1)\n", - "l1vali = np.asarray(history_L1val)\n", - "\n", - "plt.plot(np.arange(l1train.shape[0]),l1train,'b',label='Training loss')\n", - "plt.plot(np.arange(l1vali.shape[0] ),l1vali ,'g',label='Validation loss')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3Vj1E9ZdJMTU" - }, - "source": [ - "You should see a curve that goes down for ca. 40 epochs, and then starts to flatten out. In the last part, it's still slowly decreasing, and most importantly, the validation loss is not increasing. This would be a certain sign of overfitting, and something that we should avoid. (Try decreasing the amount of training data artificially, then you should be able to intentionally cause overfitting.)\n", - "\n", - "## Training progress and validation\n", - "\n", - "If you look closely at this graph, you should spot something peculiar:\n", - "_Why is the validation loss lower than the training loss_?\n", - "The data is similar to the training data of course, but in a way it's slightly \"tougher\", because the network certainly never received any validation samples during training. It is natural that the validation loss slightly deviates from the training loss, but how can the L1 loss be _lower_ for these inputs?\n", - "\n", - "This is a subtlety of the training loop above: it runs a training step first, and the loss for each point in the graph is measured with the evolving state of the network in an epoch. The network is updated, and afterwards runs through the validation samples. Thus all validation samples are using a state that is definitely different (and hopefully a bit better) than the initial states of the epoch. Hence, the validation loss can be slightly lower.\n", - "\n", - "A general word of caution here: never evaluate your network with training data! That won't tell you much because overfitting is a very common problem. At least use data the network hasn't seen before, i.e. validation data, and if that looks good, try some more different (at least slightly out-of-distribution) inputs, i.e., _test data_. The next cell runs the trained network over the validation data, and displays one of them with the `showSbs` function.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { + ], + "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 386 + "name": "supervised-airfoils.ipynb", + "provenance": [] }, - "id": "N6OONK0bL4ev", - "outputId": "240ad666-e266-42d1-c13f-3573eddc7221", - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" } - ], - "source": [ - "net.eval()\n", - "for i, validata in enumerate(valiLoader, 0):\n", - " inputs_curr, targets_curr = validata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", - " \n", - " outputs = net(inputs)\n", - " outputs_curr = outputs.data.cpu().numpy()\n", - " if i<1: showSbs(targets_curr[0] , outputs_curr[0], title=\"Validation sample %d\"%(i*BATCH_SIZE))\n" - ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "TJtxXgGoJMTV" - }, - "source": [ - "Visually, there should at least be a rough resemblance here between input out network output. We'll save the more detailed evaluation for the test data, though." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4cIO36A_FhdE" - }, - "source": [ - "## Test evaluation\n", - "\n", - "Now let's look at actual test samples: In this case we'll use new airfoil shapes as out-of-distribution (OOD) data. These are shapes that the network never saw in any training samples, and hence it tells us a bit about how well the NN generalizes to unseen inputs (the validation data wouldn't suffice to draw conclusions about generalization).\n", - "\n", - "We'll use the same visualization as before, and as indicated by the Bernoulli equation, especially the _pressure_ in the first column is a challenging quantity for the network. Due to it's cubic scaling w.r.t. the input freestream velocity and localized peaks, it is the toughest quantity to infer for the network.\n", - "\n", - "The cell below first downloads a smaller archive with these test data samples, and then runs them through the network. The evaluation loop also computes the accumulated L1 error such that we can quantify how well the network does on the test samples." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "dD5O58L9HLE-", - "outputId": "9ea61f54-2bc2-4881-f6bf-e37502e48383" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded 10/10 test samples\n", - "\n", - "\n", - "Average test error: 0.027735761646181346\n" - ] - }, - { - "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" - } - ], - "source": [ - "if not os.path.isfile('data-airfoils-test.npz'):\n", - " import urllib.request\n", - " url=\"https://physicsbaseddeeplearning.org/data/data_test.npz\"\n", - " print(\"Downloading test data, this should be fast...\")\n", - " urllib.request.urlretrieve(url, 'data-airfoils-test.npz')\n", - "\n", - "nptfile=np.load('data-airfoils-test.npz')\n", - "print(\"Loaded {}/{} test samples\\n\".format(len(nptfile[\"test_inputs\"]),len(nptfile[\"test_targets\"])))\n", - "\n", - "testdata = DfpDataset(nptfile[\"test_inputs\"],nptfile[\"test_targets\"])\n", - "testLoader = torch.utils.data.DataLoader(testdata, batch_size=1, shuffle=False, drop_last=True) \n", - "\n", - "net.eval()\n", - "L1t_accum = 0.\n", - "for i, validata in enumerate(testLoader, 0):\n", - " inputs_curr, targets_curr = validata\n", - " inputs.data.copy_(inputs_curr.float())\n", - " targets.data.copy_(targets_curr.float())\n", - "\n", - " outputs = net(inputs)\n", - " outputs_curr = outputs.data.cpu().numpy()\n", - "\n", - " lossL1t = criterionL1(outputs, targets)\n", - " L1t_accum += lossL1t.item()\n", - " if i<3: showSbs(targets_curr[0] , outputs_curr[0], title=\"Test sample %d\"%(i))\n", - "\n", - "print(\"\\nAverage test error: {}\".format( L1t_accum/len(testLoader) ))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "By722sgwFbwG" - }, - "source": [ - "The average test error with the default settings should be ca. 0.03. As the inputs are normalized, this means the average error across all three fields is 3% w.r.t. the maxima of each quantity. This is not too bad for new shapes, but clearly leaves room for improvement.\n", - "\n", - "Looking at the visualizations, you'll notice that especially high-pressure peaks and pockets of larger y-velocities are missing in the outputs. This is primarily caused by the small network, which does not have enough resources to reconstruct details.\n", - "\n", - "Nonetheless, we have successfully replaced a fairly sophisticated RANS solver with a very small and fast neural network architecture. It has GPU support \"out-of-the-box\" (via pytorch), is differentiable, and introduces an error of only a few per-cent.\n", - "\n", - "---\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vhH-rUZ-JMTX" - }, - "source": [ - "## Next steps\n", - "\n", - "There are many obvious things to try here (see the suggestions below), e.g. longer training, larger data sets, larger networks etc. \n", - "\n", - "* Experiment with learning rate, dropout, and network size to reduce the error on the test set. How small can you make it with the given training data?\n", - "\n", - "* The setup above uses normalized data. Instead you can recover [the original fields by undoing the normalization](https://github.com/thunil/Deep-Flow-Prediction) to check how well the network does w.r.t. the original quantities.\n", - "\n", - "* As you'll see, it's a bit limited here what you can get out of this dataset, head over to [the main github repo of this project](https://github.com/thunil/Deep-Flow-Prediction) to download larger data sets, or generate own data.\n", - "\n" - ] - } - ], - "metadata": { - "colab": { - "name": "supervised-airfoils.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 1 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file