tigl/python/internal-api-3-geometry-modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wing modelling example #\n",
    "\n",
    "In this example, we demonstrate, how to build up a wing surface by starting with a list of curves. These curves are then interpolated using a B-spline suface interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing modules ##\n",
    "\n",
    "Again, all low lovel geometry functions can be found in the ```tigl3.geometry``` module. For a more convenient use,\n",
    "the module __```tigl3.surface_factories```__ offers functions to create surfaces. Lets use this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tigl3.curve_factories\n",
    "import tigl3.surface_factories\n",
    "from OCC.gp import gp_Pnt\n",
    "from OCC.Display.WebGl.jupyter_renderer import JupyterRenderer\n",
    "from OCC.BRepBuilderAPI import BRepBuilderAPI_MakeFace, BRepBuilderAPI_MakeEdge\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Create profile points ##\n",
    "\n",
    "Now, we want to create 3 profiles that are the input for the profile curves. The wing should have one curve at its root, one at its outer end and one at the tip of a winglet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of points on NACA2412 profile\n",
    "px = [1.000084, 0.975825, 0.905287, 0.795069, 0.655665, 0.500588, 0.34468, 0.203313, 0.091996, 0.022051, 0.0, 0.026892, 0.098987, 0.208902, 0.346303, 0.499412, 0.653352, 0.792716, 0.90373, 0.975232, 0.999916]\n",
    "py = [0.001257, 0.006231, 0.019752, 0.03826, 0.057302, 0.072381, 0.079198, 0.072947, 0.054325, 0.028152, 0.0, -0.023408, -0.037507, -0.042346, -0.039941, -0.033493, -0.0245, -0.015499, -0.008033, -0.003035, -0.001257]\n",
    "\n",
    "points_c1 = np.array([pnt for pnt in zip(px, [0.]*len(px), py)]) * 2.\n",
    "points_c2 = np.array([pnt for pnt in zip(px, [0]*len(px), py)])\n",
    "points_c3 = np.array([pnt for pnt in zip(px, py, [0.]*len(px))]) * 0.2\n",
    "\n",
    "# shift sections to their correct position\n",
    "# second curve at y = 7\n",
    "points_c2 += np.array([1.0, 7, 0])\n",
    "\n",
    "# third curve at y = 7.5\n",
    "points_c3[:, 1] *= -1\n",
    "points_c3 += np.array([1.7, 7.8, 1.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolation of the airfoil with a B-spline ##\n",
    "\n",
    "TiGL brings many algorithms, that build curves and surfaces. The core algorithms can be found in the __tigl3.geometry__ package. These algorithms depend however on the opencascade data structures. To make it more convenient, the __tigl3.curve_factories__ package offers more python functions to create curves.\n",
    "\n",
    "The most basic function is ```tigl3.curve_factories.interpolate_points```. This takes an array of points and builds up the interpolating b-spline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `tigl3.curve_factories_factories.interpolate_points` not found.\n"
     ]
    }
   ],
   "source": [
    "tigl3.curve_factories_factories.interpolate_points?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```interpolate_points function``` has some optional parameters as well.\n",
    " - ```degree```: Controls the polynomial degree. If degree=1, the curve will be piecewise linear.\n",
    " - ```params```: Controls, at which parameter the points will be interpolated. This array must have the same number of items as the points array!\n",
    " - ```close_continuous```: If you interpolate e.g. a fuselage section, you probably want a continous passing of the curve at the start and the end of the section. if close_continuous=True, the passing will be continous. For wings, where a discontinous trailing edge is desired, it should be False."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build profiles curves ###\n",
    "\n",
    "Now, lets built the profiles curves using ```tigl3.curve_factories.interpolate_points```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve1 = tigl3.curve_factories.interpolate_points(points_c1)\n",
    "curve2 = tigl3.curve_factories.interpolate_points(points_c2)\n",
    "curve3 = tigl3.curve_factories.interpolate_points(points_c3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of the result ###\n",
    "\n",
    "Now lets visualize the interpolated curves. We are using the pythonOCC SimpleGui to draw the curve and the points. The jupyter renderer does not yet support curves and points (only surfaces). \n",
    "\n",
    "We first draw all the points without updating the viewer. This would be very slow.\n",
    "Then, we draw the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af3a50069e0c42c4838568810399dde0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Renderer(background='white', camera=PerspectiveCamera(children=(DirectionalLight(intensity=0.9,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create jupyter renderer\n",
    "viewer = JupyterRenderer()\n",
    "\n",
    "# draw the points\n",
    "viewer.DisplayPoints(points_c1, name='p1', size=5, color=\"red\", update=False)\n",
    "viewer.DisplayPoints(points_c2, name='p2', size=5, color=\"red\", update=False)\n",
    "viewer.DisplayPoints(points_c3, name='p3', size=5, color=\"red\", update=False)\n",
    "\n",
    "# get the curves as edges\n",
    "curve1_edge = BRepBuilderAPI_MakeEdge(curve1).Edge()\n",
    "curve2_edge = BRepBuilderAPI_MakeEdge(curve2).Edge()\n",
    "curve3_edge = BRepBuilderAPI_MakeEdge(curve2).Edge()\n",
    "\n",
    "# display the edges\n",
    "viewer.DisplayShape(curve1_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer.DisplayShape(curve2_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer.DisplayShape(curve3_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer._camera.fov=9.\n",
    "\n",
    "viewer"
   ]
  },
  {
   "attachments": {
    "airFoilCurves.JPG": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result:\n",
    "![airFoilCurves.JPG](attachment:airFoilCurves.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Create the surface ##\n",
    "The final surface is created with the B-spline interpolation from the ```tigl3.surface_factories``` package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function tigl3.surface_factories.interpolate_curves has many more parameters that influence the resulting shape. Lets have a look:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tigl3.surface_factories.interpolate_curves?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function to interpolate the three curves to a surface. If you want, play around with the degree and the curve parameters. Especially the second of the three parameters has a big influence. What influence do these parameters have on the final shape?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "surface = tigl3.surface_factories.interpolate_curves([curve1, curve2, curve3])\n",
    "# surface = tigl3.surface_factories.interpolate_curves([curve1, curve2, curve3], [0., 0.7, 1.])\n",
    "# surface = tigl3.surface_factories.interpolate_curves([curve1, curve2, curve3], degree=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the result ##\n",
    "\n",
    "Now, lets draw our wing. How does it look like? What can be improved?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0ff32854b8041309ca9ef830f1c1ad0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Renderer(background='white', camera=PerspectiveCamera(children=(DirectionalLight(intensity=0.9,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# draw the surface \n",
    "face = BRepBuilderAPI_MakeFace(surface, 1e-6).Face()\n",
    "viewer.DisplayShape(face, shape_color='#0070a8', transparency=True, opacity=0.4, quality=0.2, update=False)\n",
    "\n",
    "viewer"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result:\n",
    "![airFoil.JPG](attachment:airFoil.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Add guide curves #\n",
    "\n",
    "We still want have more control on the shape of the wing example.\n",
    "\n",
    "One option is to simply add more profiles and tweak their shape. Another, more elegant way is to additionally use some guide curves for e.g. the leading and trailing edges.\n",
    "\n",
    "In this example, we are tweaking the leading edge and using our curve network interpolation algorithm that is based on gordon surfaces. Gordon surfaces are generalization of coons patches. Where coons patches can only interpolate two profiles and two guides at a time, gordon surfaces interpolate all curves at once globally. The benefit of gordon surfaces are better / smoother resulting surfaces.\n",
    "\n",
    "If you want to read about them im more detail, have a look into _\"The NURBS Book, 2nd Edition\"_, Chapter 10.5 (Interpolation of a Bidirectional Curve Network): https://www.springer.com/de/book/9783642973857"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, lets create a copy of the airfoil curves ###\n",
    "\n",
    "Lets shift the airfoil points by 3 along the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_c1[:,0]+=3\n",
    "points_c2[:,0]+=3\n",
    "points_c3[:,0]+=3\n",
    "\n",
    "# interpolate the three point sets to new profile curves\n",
    "profile1 = tigl3.curve_factories.interpolate_points(points_c1)\n",
    "profile2 = tigl3.curve_factories.interpolate_points(points_c2)\n",
    "profile3 = tigl3.curve_factories.interpolate_points(points_c3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create guide curve points ###\n",
    "\n",
    "Now, lets define some points on the guide curves. __Important: profiles and guides must intersect each other!!!__\n",
    "\n",
    "Therefore, we explicitly add points from the profiles to the guides curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# upper trailing edge points\n",
    "te_up_points = np.array([points_c1[0,:], points_c2[0,:], points_c3[0,:]])\n",
    "\n",
    "# leading edge points. \n",
    "le_points = np.array([\n",
    "    points_c1[10,:],    # First profile LE\n",
    "\n",
    "    [3.35, 2., -0.1],# Additional point to control LE shape\n",
    "    [3.7, 5., -0.2], # Additional point to control LE shape\n",
    "    \n",
    "    points_c2[10,:],   # Second profile LE\n",
    "    points_c3[10,:], # Third profile LE\n",
    "])\n",
    "\n",
    "\n",
    "# lower trailing edge points\n",
    "te_lo_points = np.array([points_c1[-1,:], points_c2[-1,:], points_c3[-1,:]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will interpolate the guide curve points to obtain the leading edge, upper trailing edge and the lower trailing edge. Lets define also the parameters of the points to control the shape of the guide curve This is optional, but can improve the result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "te_up = tigl3.curve_factories.interpolate_points(te_up_points, [0, 0.65, 1.])\n",
    "le = tigl3.curve_factories.interpolate_points(le_points, [0., 0.25, 0.55, 0.8, 1.0])\n",
    "te_lo = tigl3.curve_factories.interpolate_points(te_lo_points, [0, 0.65, 1.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check: Draw the guide curves ###\n",
    "\n",
    "Now lets draw the curves. You can still tweak the leading edge, if you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d39f13d715da42b7bf2d9e6afa7d5ede",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Renderer(background='white', camera=PerspectiveCamera(children=(DirectionalLight(intensity=0.9,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the curves as edges\n",
    "profile1_edge = BRepBuilderAPI_MakeEdge(profile1).Edge()\n",
    "profile2_edge = BRepBuilderAPI_MakeEdge(profile2).Edge()\n",
    "profile3_edge = BRepBuilderAPI_MakeEdge(profile3).Edge()\n",
    "\n",
    "te_up_edge = BRepBuilderAPI_MakeEdge(te_up).Edge()\n",
    "le_edge = BRepBuilderAPI_MakeEdge(le).Edge()\n",
    "te_lo_edge = BRepBuilderAPI_MakeEdge(te_lo).Edge()\n",
    "\n",
    "# display the edges\n",
    "viewer.DisplayShape(profile1_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer.DisplayShape(profile2_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer.DisplayShape(profile3_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "\n",
    "viewer.DisplayShape(te_up_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer.DisplayShape(le_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer.DisplayShape(te_lo_edge, update=False, shape_color='#2cd342', quality=0.05)\n",
    "viewer._camera.fov=9.\n",
    "\n",
    "# also draw the guide curve points\n",
    "viewer.DisplayPoints(te_up_points, name='p1', size=5, color=\"red\", update=False)\n",
    "viewer.DisplayPoints(le_points, name='p2', size=5, color=\"red\", update=False)\n",
    "viewer.DisplayPoints(te_lo_points, name='p3', size=5, color=\"red\", update=False)\n",
    "    \n",
    "viewer"
   ]
  },
  {
   "attachments": {
    "airFoilGuideCurves.JPG": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result:\n",
    "![airFoilGuideCurves.JPG](attachment:airFoilGuideCurves.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the gordon surface ###\n",
    "The final surface is created with the Gordon surface interpolation from the ```tigl3.surface_factories``` package.\n",
    "\n",
    "Gordon surfaces are surfaces, that passes through a network of curves. Since we just created this network with 3 profiles and 3 guides, we can pass those to the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "surface = tigl3.surface_factories.interpolate_curve_network([profile1, profile2, profile3],\n",
    "                                                            [te_up, le, te_lo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the result ###\n",
    "\n",
    "Now, lets draw our wing. How does it look like? What can be improved?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "978c9596031d4868bd76030f865a8d0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Renderer(background='white', camera=PerspectiveCamera(children=(DirectionalLight(intensity=0.9,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "face_gordon = BRepBuilderAPI_MakeFace(surface, 1e-6).Face()\n",
    "viewer.DisplayShape(face_gordon, shape_color='#0070a8', transparency=True, opacity=0.4, quality=0.2, update=False)\n",
    "    \n",
    "# match content to screen and start the event loop\n",
    "viewer"
   ]
  },
  {
   "attachments": {
    "airFoilGordon.JPG": {
     "image/jpeg": 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ozQA/PvRn3pmaM0APz70Z96ZmjNAD8+9GfemZozQA/PvRn3pmaM0APz70Z96Zmq99e29hbPcXcqxRL1Zu/sPU+1JtRV2NJvRFonAySMCuL8Q+JmuC9npD/uyNslyvf2T/AB/L1rL1vXbnW3MEAaDT8/c/ik56t6D2/OksrMIBxXzmYZrzfu6D07/5Hq4bBW9+p9xVt7MIgAFWPs3tWolvxUv2f2rwbnplzwbbmNrmUjAICj+ddP8AjWdo0PkWI7FyWNXs19pl1P2eGgn6/efP4ufNVkx+fejPvTM0ZruOYfn3oz70zNGaAH596M+9MzRmgB+fejPvTM0ZoAfn3oz70zNGaAH596M+9MzRmgB+fejPvTM0ZoA0NL5TU+f+XU/+zUUmj8x6p/16/wDxVFS2Fg1v/VaV/wBev/xNZuav6zIjx6WEOStrg+33azs1Q9x2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRUF3cQ2du891IsUKjJdzgCuB13xpPeboNGDwQng3DD52H+yOw9zzXLiMXSw8bzfyNaVCdV2ijqfEPiW00cGIEXF6RlYFPT3Y9hXCXNzeazdeffyFyD8kY4SP6D+vWqdna5bc2WZjksTkk+pPety0gAA4r5jGZhUxLttHsezh8LClruySytgoHFa0EWAOKit0xitCFa806xY4s1Zig3MBjrT4kzV2BNvNb4Sg8RWjTXX8jKvUVKDmWFAVQq8ADFGabmjNfdpJKyPm3rqx2aM03NGaYWHZozTc0ZoCw7NGabmjNAWHZozTc0ZoCw7NGabmjNAWHZozTc0ZoCw7NGabmjNAGpov3NV/69f/AIqio9HlSNNTDnBa2wPf71FS0NMrahx9i/64f/E1VzVnUf8Aly/64f8AxNVM1pPciGw7NGabmjNSWOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabWRrviLTtFGLubdORlYYxuc/h2+pqJzjBc0nZDjFydkjZzXLeIPGdnpzNBZAXl4OCFPyIf8Aab+g/SuL1zxRqWt5iz9ktD1hjblv95up+gwKzLa2CgADFeJis33jR+89GjgetT7jTvda1LVjJ9vlRxINu1UChFyDtX05A96sW1ut1eQJDGkEIVUJeQZ68sScAnn9KrW8IHatCFMYr56TbPUhaKskS2iqwyhDDOMitOBelVoB5YkMYVZHXbvxkgenpVy3+WJTMyLIzYVQwO4evt9DWbZpyp6xLkIq/CKpw1eh7UEouwjpVkcDFQwgBQe9SZr6rKMH7Gn7Wa96X5Hi47Ee0lyR2Q7NGabmjNeycI7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabRmkItWPJu/+uP8A8VRRp/8Ay+f9cP8A4qinLZEdRdU/5cf+uH/xNUs1c1b/AJcf+uH/AMTVHNOW5Udh2aM03NGaRQ7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5qnqeqWWlwedf3CQp23HlvoOpqZSUVdgk27IvVQ1fV7HSYDLf3CR8fKnVn+g6muC13x5c3OYtFQ28XQzyAFz9B0H45/CuSKyTzNNO7yzN953bcx/E15WJzWEPdpav8Dto4KUtZ6HU6544vr8NDpqNZQH+POZT+PRf1PvXNRQlnLOWZ2OWZjkk+5qSKGrcceK8Gviald3m7npU6UaatFDYYcVeijpsa1ZjXFcrdzYkiWrkQqugq1GKQyxEKtoqsNrqGXuD3qrF2q3FUspO2qL0bMzFpNoiVQFChiwwMAY5yPetSG3dXIkBXH6/Ss+wSR5A0ZK7TywOMVts5YKP4VGAPSvTyvL51qiqzXuL8f+AcWNxXIuSO4ZozTc0Zr688QdmjNNzRmgB2aM03NGaAHZozTc0ZoAdmjNNzRmgB2aM03NGaAHZozTc0ZoAdmim5rO1LUPKJgtsGc9WxkJ9ff2rDEYinhqbqVXZIyrVqdCm6tV2ii891bpMsDXEQnbpGWGf/ANftUtclJbLNGUlHmAnJ3c5Pr9fetvR7kGEWzl2kUFgWOTjgY6V4WXZ8sVW9jOOrbt6eZ5OW59h8wqujCLjLW1+qX5Py/E3tM/5fv+uH/wAVRRpPIv8A/rh/8VRX0stkeu92N1jpYf8AXD/4mqGRV/WeBp//AFw/+JrOzRLcqOw7IoyKbmjNIodkUZFNzRmgB2RRkU3NGaAHZFGRTc0ZoAdkUZFNzRmgB2RRkU3NGaAHZFGRTc0ZoAdkUZFNzRmgB2RRkU3NGaAHZFGRTc0ZoAdkUZFNzRmgB2RRkU3NGaAHZFGRTc0ZoAdkUZFNzWZrGv6bpAxfXSrIRkRL8zn8BUSnGCvJ2Gk5OyNXIqjq2r2Okw+Zf3CRA/dXqzfQDk157q/j2/umaPS4haQnje4DSH39B+tco/mTzNNcSPLM3LO5JJ/E15WIzWEdKSuzspYKUtZ6HZ6z4/ubhTHo9ubdTx502C/4L0H45rkZmmu7gz3Usk0zdXkbJpUjqdI8V4tfFVK799no06MKfwoZHFg9KspHxSqtTKtcrZskKi1Oi01RUyCoGPQVOgqNamSkxkqVZjqBKnSkBZjq/YwPcSBU6DqewqtZWsty+IhwOrHoK6a3iS3iCRjgdT6mvSy/L5Yl889I/mcuKxSpLljuOt4kgjCJnHUk9zUmabmjNfWwioJRirJHiNuTux2RRkU3NGaoQ7IoyKbmjNADsijIpuaM0AOyKMim5ozQA7IoyKbmjNADsijIpuaM0AOyKM00tgEk4A6msu6vWuMxW5Ij7v0Lew9veuPGY6lg4c9V+i6v0OXF4yjg6ftazsvxfoS318ctDaN8+cNIOi+w9/5VThg2jgfj6+5qa3gCgAAAVbSKvhcbjKuPnzT0XRdj89zLMa2ZT97SC2X6vuyoIs1Jap5d/EfVHH/oNXkhpJE2XsQA6RMx/EgD+RroyihbFwa/rQ7OHcO1jYy7J/kzX0bkah/17/8AxVFGh8jUf+vf/wCKor76eyPvHuxmt9NP/wCuH/xNZua0td6ad/17/wDxNZWac9yo7D80ZpmaM1BQ/NGaZmjNAD80ZpmaM0APzRmmZozQA/NGaZmjNAD80ZpmaM0APzRmmZozQA/NGaZmjNAD80ZpmaM0APzRmmZozQA/NGaZmjNAD80ZqOSRI0LyMqIOrMcAVyur+OdNs8pZBr6bp+74QH3Y9fwzWdStCkrzdioU5T+FHXZrD1rxVpekhllm864H/LGHDNn37D8a831nxPqurEq8xtrftDASv5t1NY8cIUcCvKr5stqS+bO2ngXvNnT6x421S/Zks8WNueBs5kI927fhXNBCzl3JZ2OWZjkk+5qVI6lVMV49XETqu83c74U4wVoojSOp0jpyrUqrXO2aiItSqtCrUij1pDFUVIopFFPWpAcoqZe1MUVIopDJFFTJUSiplpDJVrT0uwe7bP3YgcM39BUmlaQ04EtzlIuy9C3/ANaujUBFCqAFAwAK9nAZW6tqlbSPbucGJxih7tPcIIkgiEcS7VFSZpmaM19JGKirLY8htt3Y/NGaZmjNMB+aM0zNGaAH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmmTSpCheRgFFVrq9SDKr88v8AdHb61mM0k8m+Ztx7eg+leJmWd0sHenD3p9u3r/keDmufUcAnTj71Tt29f8t/QnuJ5Ltsfci/u+v1qeCLFRwpVuIV8bUr1MVU9rWd2fB1sTWxtT2td3f5ehLGlWo0qKOrcddFOKOmlBEsEXtWfKwe9uHH3QRED67ev6lq0p5/slq8wAL/AHUU92PQVkxrsRVznHf1PrX0uT4f3nUfQ+zyLDckJVn10X5v9DW0PP8AxMP+vf8Ao1FM0Rsfb/eD+jUV9FLZHtPcd4g6ab/17/8AxNZOa1fERGNN/wCvc/8AstY+aJblR2H5ozTM0ZqSh+aM0zNGaAH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAH5ozTKKAH5ozWFq3ijStMLJLciWYf8sofnb8ew/GuM1bxzqN5lNPjFlFn7/DyEfyH61y1sZSo7vU2p4ec9kejahqNnp0XmX1xHAvbe3J+g6muO1X4gRjKaTatKe0s/yr+C9T+lcFKZLiYy3Ekksp6vIxY/macsdeTXzWctKasdtPBxj8WpY1PUr/AFZ92oXLzAHIToi/RRxVdI8dqlVKkVa8uVSU3eTuzsjFRVkRqlSqlPC04Cs2y0hFWpFWlAxTwKVx2ACngUAU4UhigU8Ugp61LAVakWmipFFJjHKKkUUxe1aOmabPfN+7G2MdXYcfh61dOnKpLlgrsmUlBXkyCCN5XCRKXc9ABzXU6Vo62+2W5AeXqF7L/iat2FjBYx4iXLkYZz1NWs19HgsrjS9+rrL8EeViMY5+7DRD80ZpmaM165wj80ZpmaM0APzRmmZozQA/NGaZmjNAD80ZpmaM0APzRmmZooAfmjNMqrcXscXCfO/oOgrDEYmlhoc9WVkc2JxdHCQ9pWlZFwsqjLsqj1Y4FZN5qmWZIVdI/wDnoy9R+HT8agmlknbMjZ9B2FCrXxmY8RVK7cMP7se/V/5Hw+YcVyrXpYeNovrtL5dvzEiAPzDkHnIOc1ajWqwgGS0R8tz6Dg/Ud/51NFLhwkoCOeAR91vof6V4MY31Wp88sOp+9Td+66/8H1XzsXEqwhqstToa3gXTZajNW4TzVFGpLi4YAQxEiVxyw/gX1+vp/wDWr0MNTlVmoQ3Z62AoTxNVUobsfdz/AGi4AU5hhJC/7TdCfw6fnTM1GiqiKqjCqMAUua+7w9FUKagj9Gp040oKnDZf1+JqaL1vf+uQ/wDZqKZoxObzH/PIfyaiumWyF1Y7xACpsN3eE4/8drKzWz4n4XS/+vc/+y1iZonuVHYdmjNNzRmoKHZozTc0ZoAdmjNNzRmgB2aM03NGaAHZozTc0ZoAdmjNNzRmgB2aM03JrE1bxRpemFkluBLMP+WUPzt+PYfjUzqRgrydhxi5OyRu5qG7u7ezhMt3PHDGP4pGAFec6p46v7nKafClpGf42+d/8B+tcvcST3cvm3c0k8n96Rix/wDrV51bNKcdKauddPByestD0HVfHtpDlNMhe6f++3yJ/ia5DVPEOrarlbm6aOE/8sofkXHoe5/E1mqntUioK8mtjqtXd6eR208PCGyIUiCgADFSqlSKtSBa42zewwJT1WnBaeBU3KGhaeBS4p4FSMbj2p4FLSikAAU8CgCnCgYAVIKaKeKQAKeBSLTxxSAUCpokaRwsalmPAAGSauaZpFxfYYDy4f8Ano3f6etdbp9hb2K/uUy56u3JNehhMtqYj3npHv8A5HNWxcKWi1Zk6XoOMSX/AOEQ/qa6JAqKFQBVHAAGAKTNGa+kw+Fp4ePLBHk1a0qrvJjs0ZpuaM10GQ7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaZLMkQy7Y9u9RUqQpRc5uy8zOrVhSi51Gkl1ZLmop7iOEfO3PoOtUZr13GIxtHr3qp3PqfWvl8fxNCF4YVXfd7f8E+QzHi2nTvTwa5n3e33dSxcXck2QDsT0HX86gUUuKcBXyGIxNXFT56srs+HxOLr4yftK8m2Kop602pFrNGUUOWnFQ6lXUMp4wRnNIKetax0N6bcXdDF3w9AZIvTqy/4j9frVuJ1ZQyEFT0IOc1EKZsCSqyNtZ2wU7Of6H3/OumCc3ZLU9ShF4yappe+9vP1/z+/uW5JvKAwu5ycKoPU/0FES7ASzbpGOWbHU/4U0Q+VM7Fw7k43DoB6D/PNPya+4ynL1hqaqT+J/gfoOVZZHL6dnrN7v8AReX5js0ZpuaM17B6hqaJyb3/AK5D+TUUaDyb7/riP/ZqKt7Ize7JvFP3NK/69z/7LWFmtzxX9zSv+vc/+y1g5pS3KitB2aM03NGakqw7NGabmjNAWHZozTc0ZoCw7NGabmmvIsaF5GVEHUscAUBYkzRmuZ1PxlpVnuWF2u5RxthGV/76PH5VympeMtVvCVtilnF6R/M34sf6AVx1cdRpdbvyN4Yac+h6RfahaWEe+9uYoFPTe2CfoO9cnqfj2BMppls8zdpJfkX8up/SuCcPLIZJneSQ9WdixP4mlVK8ytmlSWkFY7KeCivi1NDU9d1TUyftN06xn/llF8i/p1/HNZqxgAADAqZUp4WvNnVlN3k7nXGKjoiNUqQJTwKdisrlpDQtOC04LTsYpDEVacBSgU6lcBKcBRTgKQwAp1FLSAKdRinAUDACnAUCnAUgFxTgKWGN5ZAkSM7noqjJro9N8OkgPftgdRGh5/E1vQwtTEO1NGdStCmryZiWdpPdybLeMue57D6mup0zQYbfEl1iaUdv4R/jWpBFHBGI4UVEHQAVJmvoMLldOj70/ef4Hl1sZOppHRDs4GAOBRmm5ozXqHGOzRmm5ozQFh2aM03NGaAsOzRmm5ozQFh2aM03NGaAsOzRmm5pkkyR/eIz6VnVrQox56jSXmZVq1OhHnqySXmS5pskqRjLkCqMt4zcRjaPU9arHJOSck9zXzWO4mp0/dwy5n3e3+bPksw4upU7wwi5n3e3+b/AtzXrHIiG0ep61UJJOWJJPc0U6vksVjq+MlzVpX/L7j4nGZjicdLmryv5dF8hMUoFLSiuQ5EgFOooqikhwp4ptPFWjRCinrTQKa7kMFQbpCMgDt7n0Fb0qcqslCCu2dWGw9TEVFSpK7Y95BGBwSzcKo6sakgiKnfLgykY46KPQU2CHyzuc75TwW/oPQVNmvusqyiOEXtKms/y/rufpWUZPTy+HNLWo9328l/n1HZozTc0Zr2z2bDs0ZpuaM0BY2fDo3DUD6Qg/wDoVFO8Mcrqf/XAf+zUVT2Rm92O8V/c0r/r3P8A7JWBmt7xacJpP/Xuf/ZK5/NKW5cdh2aM03NGaVyh2aM1Rv8AVLPT0LXlzHF7E/MfoOprl9R8dxKCum2zyt/z0m+Vfy6n9Kwq4mnS+NmkKU57I7bNZWp+ItM00lbm5Uyf884/nb8h0/GvNtR17VNRJ8+6dIz/AMs4TsX9OT+JrMSMDoK86tmq2pr7zqhgv52dhqXju5l+XTbZYV/vzfM35DgfrXMX17eai+6+uZZjnO1j8o+i9BUSpTwteXVxVSr8bOyFGENkRKgqRUqQLTgK5nI1sMC08LTsU7FIaQ0LinAUuKdikMQCnYpaWlcBBTsUUuKQBTsUAUoFAwAp1FLSAKdSAU6gYCnAUKCSABknoBW3p+gXNxh7j9xH7jLH8O1aUqFSs7U1cidSNNXkzHRS7BVBZj0AGSa3dO8PzTEPdkwx/wB0csf8K37DTrWxH7iMb8YLtyx/Grua9zDZRGPvVnd9uh51bHN6U9CGys4LKPZbxhR3Pc/U1YzTc0Zr2IxUFyxVkcLbk7sdmjNNzRmquIdmjNNzRmi4Ds0ZpuaM0XAdmjNNzRmi4Ds0Zpm73qKS5Veh3H2rnxOLo4WPNWkkcuKxtDBx5681Ff10LGaY88aBtzcgcAcnNUZJ3fqcD0FR4r5bHcUfZwsfm/8AL/M+Ox/GO8cHH5v9F/XoTSXLtwvyj9ah+tFLXy2IxVbEy5q0m2fG4rG18ZLnrzcmJS0uKWsLHMkAopaUCnYpIQU6ilplJCU+inYqki0gFOAppIVSWIAAyeeBTRun6Fkh9ejN9PQfr9K7sHgauLnyUl8+iPVy3Kq+YT5aa0W7ey/rsKXZ2McABI4Zz91f8T7VYhjWIHGSx5Zj1Y+9IgVECoAqjgAdqdmvvMvyylgY+7rLqz9Ky7LKGX0+WktXu+r/AMl5fmOzRmm5ozXp3PQHZozTc0ZouA7NGabmjNFwN/wryuqf9cB/7NRR4S5XVf8Ar3H/ALPRTlsjN7sTxgcJpP8A17n/ANkrmrq4itEdrqRIVQEsXOMAda6jxW23+ycYDG3YAlsYOE715brktvaalfebawGacMG3YZl3HO05zhc9uhGK83H4+WFmoqN7rc7cJhlWjfmtYk1HxtYQArZJJdyDjIGxPzP9BXMaj4q1a+JCSi1j/uwjB/Fuv5Yplhpltc322WcohDEKBgscE4B6D8aznjCyMquJFB4cKV3e+DyK8mrmFWr1t6HoRwcKeyuQFSzl3JZ25LE5J/GnhKkC04LXG5GuwwLTgtSAUAVLYxAKUCnYpwpDG4pwFKBSgUhiAU4ClxRQAYpRS0uKQCUtLilApDEpwFAFOoAKWjFLSAKUCnRo0jhY1Z2PZRk1uWPhyeQBrpxCv90ct/gK2o4epWdqauROrCmryZiIpdgqglj0AGSa2bDw/czkNc4gj9Dyx/CuksbC2slxbxgN3Y8sfxq1XtYfKIx1rO/kcFXHN6QRVsdOtbEfuIxv7u3LH8au7qZRXrwhGC5YqyOCUnJ3Y/dRuplFUIfuo3UyigB+6jdTKKAH7qN1MooAfuo3VEzqv3jzUTzk/dGPrXm4zN8LhNJyu+y1f9ep5GOzvB4HSpO8uy1f9epZLgDJIA96he5HRRn61WJLHJJJor5bGcTV6nu0Fyr73/kfG4/i7E1rxwy5F33f+SHPI7/eP4U2ilr5ypUnVlzTd35nytWrOtLnqSbfnqJS0oFLU2ISExS0tFOxSQUU7FFMdgopadimVYTFOxS4pwHUnAA7mrUTSMW3ZCYpsjrGQMFnPRR1P+A96b5pk4gxt7ysOPwHf+X1pyIqA4ySeSx5J+pr6HLciqV7Tre7H8WfYZVwxOrarjPdj26v17fmIsRYh5yGIOQg+6v+J96n3UyivtKFCnh4KFNWR9zSpQowVOmrRXRD91G6mUVqaD91G6mUUAP3UbqZRQA/dRuplFAHSeEDlNW/69x/7PRTfB33NW/69x/7PRVS2Rm92N8YE7dKz/zwP/slcvcWdrdljcxBpPLZFkH3lypH4jnvXSeMHLDSwR0gI/RK57NRiKUaqcJq6ZrSqSp+9FnBanod5b3W1Yg8Mj4R0+6uTwDnp9T+dYrfeAdT64PBr1lJCjZX8R2I9D6iud1jw1DeM0tq/kznsxyrf4V4WJyuUW5Udux6tHGqTtL3X+H/AAPyOS1S4tZjB9ktzEqxKrfNnkDkdOef4u/pxVQYxmtHXNGuNIaETlW8xFOVORkjJAPce9QaVdw2c0rzW6zbo2UZLcEqR2I6569R2ryGmjtvFrUgwKKN6u7FVCZOQoOQB6DNOxSE4uO4gFOApcUuKLiEApcUuKWkAmKWilpAFLRilxQMKWlxS4oAKWrdlp11ecwRHZ/fbgVv2Xh2GPBunMrf3V4X/E11UMFWr/CtO5jUxFOnuzm7a3muX2QRs7f7I6fWt6w8OMSGvZMD/nmnX8T/AIV0EUaRJsiRUQdlGBT817OHyqnT1qav8DgqY2ctI6EdrbQWqbbeJYx3x1P1NT5pmaM16kYqKskcbbbux+aM0zNGaYh+aM0zNGaAH5ozTM0ZoAfmjNMzQWA68VM6kaceabsjOpUhSjzTaS8x+aM1A0v90fiaYzFupr5/F8SYajdUvff3L7z5nG8WYSheND339y+//InaVR7n2qJpWb2HtTKK+WxmeYvFaOXKuy0/4J8djuIcbjNHLlj2Wn/BExS0UuK8k8PcSlxSilp2KSEFLS0U7DSCinYoplWDFFLS0x2Ep2KXFOAppFpDdtPVaRykSFpGCqOpJqAyyS8RZij/AL5HzH6Dt+P5V3YTAVsVLlpRv+R6uX5RiMfL91HTq3sv67LUmllWI7cF5COEHX/61RbGl5nwe4jH3R/jQiKgIUYyck9yfUmnZr7TL8lo4W05+9L8F6H6BluSYfL0pL3p93+i6fn59B+aM0zNGa9o9kfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAOk8HsQuq47wD/2eik8G/d1X/rgP5PRVy2Rm92J40AVdJx3gP8AJK5vNdL42+5pH/Xuf/ZK5jNKe5Udh2fejNNzRmpKCZEnjMcyCRCMFWGQRWDceErOUXElvK0T+WdkTcru4wQc59eP5VvZozWFfDU68bSRtSxE6WkXp2PLr6znspjHcxPGc8Ejr9DVy9vbOWG2S3tjEyJh237udzHB456g5/DtXoNxDFcRGOeNZIz1VhkVh694eiuIIv7NVImiXaEYtjGWOAST3bPP8q8XE5XOLvT1R6dHGU5+7LTy6ff/AJnJilxWhYW72VtqEd5psskixgg7iBjegwuAee+RnjIx3rOiYSLlTn1rymnF2aOtx3a2HUuKKWpIEpaUCp7a0nuWxbxO/uBwPxqoxcnaKuDaWrIcUqgkgAZJ7CugsvDjHDXku3/Yj5P4mt20s7e0XFvEqn+91J/GvRo5XVqaz91ficlTGQjpHU5iy0K7nIMgEMZ7t1/Kt2y0WztsMyec4/ik5H4CtLNGa9ehl9Gjra78zhqYqpU62Q7PpwKM03NGa7jnHZozTc0ZoAdmjNNzRmgB2aM03NGaAHZozTScdaRnH8GTx39a4MVmeGwn8Wevbdnm4zN8Hgv401fstX9w/NNLgd/yqMknrRivmsXxTOXu4aNvN/5HyON4xqSvHCQt5vf7tvzHGRj04pvU0UV81iMXWxMuatJs+SxWOxGLlzV5uT/roFFLS1zpHLYSlpRRTKsIKWilpjSEpaUClpjDFFFOplJDadilxTgKaQ0hKcBSqtRS3CRkouZJR/Avb6ntW1KjOrJRgrs68Ng62Kn7OjFyfkTKtV2ug2RbKHP98/cH+P4fnULh5/8Aj4I2/wDPNfu/j6/54qQcV9VgOHdp4l/Jfqz7jLeFqdO08W+Z/wAq2+b6/LT1Gqnzh5GMkg6E9voO1SZpuaM19TTpQpR5IKyPq4xjCKjFWS6Ds0ZpuaM1ZQ7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQB03gw/Lqv/XAf+z0Ungv7urf9cB/J6KuWyM3uxfHH3NI/64H+SVy9dP45PyaP/wBcG/klctk0p7lQ2HUU3Joyam5Vh1FNyaMmi4WHUU3JoyaLhYex3QvE3KN1GenuPQ+9ZNnoFraNeTWpXe0DrHHKu7DEY4bI45ORzkdxWnk0ZNcuIwlLEJqS+fU6KOJqUfhenY5GTQdRFnJdyWpjG4sI1wcpkDcME92Ax1/nTbPRbu4wzKIUPd+v5V2sVzNFE8ccjKj9QDj0/wAKizXn0soSk3Ud10t+p0Tx8pfCrGXZ6FaQYMoM7/7XT8q1VUKoVQFUdABikyaMmvVpUYUlaCscU5yn8THUU3Joya1uRYdRTcmjJouFh1FNyaMmi4WHUU3Joz71jWxFKhHmqySXmYV8TRw0eetJRXmOopuaK+fxXE2Hp6UU5P7l/mfMY3i/DUrxw8XN/cv8/wABcikyaSlr5rFZ5i8To5cq7LT/AIJ8ljeIsdjNHLlj2Wn47iUtFFeTueG3d3YUUtKBQkOwlKKUUUxpBRRS4pjEpcUU7FMdhKWiimMKWlxS0WKSEp1KBmmyyRwAGVsZ6DqT9B3rSFOU3aKuzajQnWkoU0230Wo8CmTTxwYDklyMhFGWP4VWeeWQ/IPJT16uf6D9aaiKgO0YzyT1J+p719HgeHqtT3q/urt1/wCAfY5dwpJ2njHZfyrf5vZfj8hzvNN95jEh/gQ8n6n/AApUVUXaihR6CkyaMmvrMNhKOFjy0o2/M+zw+GpYaHs6MVFeX69x1FNyaMmuq5tYdRTcmjJouFh1FNyaMmi4WHUU3JoyaLhYdRTcmjJouFh1FNyaMmi4WHUU3JoyaLhYdRTcmjJouFh1FNyaMmi4WOo8E/d1b/rgv8nopPBH3dW/64L/ACeirlsjN7sb43bKaT7QN/JK5fNdJ41yBpmf+eTfySuZzSnuVDYdmjNNzRmoLHZozTc0ZoAdmjNNzRmgB2aM03NGaAHZozTc0ZoAdmjNNzRmgB2aM03NSCGZoGkihklwcBUGSfX/ACaipUhSjzTdkCVxuaM0rJJEoaeGSMepGQPrjOKFKsuVIKnuDxXh4viLDUNIJyf3L7z5zMOJMPg9IxcvNbff/kHNLiiivncVxHiq2lP3F5b/AHnyOM4sxuI0pWgvLV/e/wDgC0lLRXh1Ks6suao235nzlWtUrS56km356hRRS1FjMSlopaBiUtLiimMQUtFFMdgopaKBgKWilphYKKKWmVYSnClxTZZEhUGRguemep+nrVQg5O0dWa0qM6slCmrt9EPxTZZI4V3SsFz0B6n6DvVWS5lkJEQ8pP7zDLfgOg/HNRKiqxbkuerMck/jX0OC4erVfere6vxPr8v4TqTtPFy5V2W/+S/F+RM9zLJxGPKT+8wBb8ug/Wo1UKSeSx6sTkn8aM0Zr6zCZfQwi/dR179T7TCYGhg48lCKX5v1e47NGabmjNdp1Ds0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AOzRmm5ozQA7NGabmjNADs0ZpuaM0AdV4HPy6t/1wX+T0UeBRuTV+ekC/wAnorR7IxluyPxyeNK/64t/JK5fNdR47+7pP/XFv5JXK5NKe5cNh2aM03JoyagsdmjNNyaMmgB2aM03JoyaAHZozTcmjJoAdmjNNyaMmgB2aM03JoyaAHZo3MB8jspyDkeo6U3JoyamUIzXLJXQFhL1mYJMQr9AezfT/CkeJWJZco56svGfr2NVmAZSGAIPY0K8kXT94n91jyPof8fzr4/MuH6kW6mF1Xb/AC7nxeZ8NTi3Wy+Vm94vb5dPk/vJt7p/rACv99f6jt+tSKQygqQQeQRTYZklB2HkdQRgj6ikMeCWiOxj19D9R/WvlZw5XyzVmfFVqSU3TrR9nNeWnzXT5fcSUtRpJkhXGx+wz1+nrUtQ00clSlKm7SExS0UUWIClFAooGFFFFMYUtJTqACiilplBRRUcs8cJw5+bsgGW/KrhTlUfLBXZtQw9XET9nSi5PyJqjlmjgAMjYJ6DqT9BVSS4mk4XEKe3LH8eg/zzUaqqkkDk9SeSfqa+jwXDlaraVd8q/H/gH2GA4RnK08ZLlXZb/N7L8Sd7mWQYjAiX1blvy6D9aiVQGLZLOerNyT+NGTRk19Vhcvw+EX7qOvfqfZ4TA4fBx5aEFH836vcdmjNNyaMmu06h2aM03JoyaAHZozTcmjJoAdmjNNyaMmgB2aM03JoyaAHZozTcmjJoAdmjNNyaMmgB2aM03JoyaAHZozTcmjJoAdmjNNyaMmgASRJATG6sBwdpzTs1WtI2iQ5dH3HOUXH9TU+TQA7NGabk0ZNADs0ZpuTRk0Adf4B+5rP/AFwX+T0Unw//ANXrP/XBf5PRVy2RjLdkPjz7uk/9cW/klcpmul8ck/8AEsz08psfklcvmip8RcNh+aM0zNGagsfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmjNMzRmgBWUMQTkMOjA4I/GnpNJHxIPMX+8o+Yfh3/Co80ZrhxmW4fGK1WOvfqceNy/D46HJiIX8+q9H/SLatFcRnBDr3Hp/gaTLxdcyR+vVh9fX+f1qmVBbcCVf+8pwf8A69SpcunEq71/vIOfxH+H5V8fjeH8Rh7ype/H8fu/y+4+Lx3C+Iw6bwv7yH8r3+X/AALPyLisGUMpBB6Ed6cKroVcGS2deTyOxPv6GpY5A+Rgq46qeo/z614MotaHydbDOF3G+m6e69V+v5ElFFFQcyCiiimAopaQkYye1VXvYxxCDK3qPu/n/hmtqOHqV5ctKLbOvCYHEYyXLQg5f11eyLlV5bqONtuS7j+FRk/j6fjVKR5Zc+Y+FP8AAnA/E9TSKAowoAHoK+mwfDU5WliZW8lv959ngOD0rSxk7+Uf1f8AkvmSvPNKTk+Uvoh5/E/4UxFVAQoAz196TNGa+ow2CoYVWoxt+f3n1+GwtHCw5KEVFeX69X8x+aM0zNGa6joH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmjNMzRmgB+aM0zNGaAH5ozTM0ZoAfmsrxTd3Vn4fvp7CNpLhY/lCjJXPBbHsMn8K0s1W1I/8S67/AOuL/wDoJrbDyUa0HJXV1p8xPYwfhtfXF54bRblWIgcxRSk5EiDp+XT8K6rNcl8Lz/xRdl/vy/8AoZrpridYImduSBwvdj6CujN5wji60rcqUn+ZmpqFPmk9EifNRrOrTNEA25eT6D0/P+hrOstSlur54Ps2xUXczF8keg6VoKiq7uM7mxnJJ6e3avNpVoVo88HdCoV4V489N3RLmjNMzRmtDY7HwA2E1jjrAv8AJ6KZ8Pz8usf9cF/k9FaS2RjLdjfiBjbpGP8Ani38krka6rx6Co0rPeJsfklcnn3qZ7mkVZDqKbn3oz71JVh1FNz70Z96AsOopufejPvQFh1FNz70Z96AsOopufejPvQFh1FNz70Z96AsOopufejPvQFh1FNz70Z96AsOopufejPvQFh1FNz70Z96AsKVG7cpKv8A3lOD/wDXp/ntwJlJx0kj+8Pw/wA/So8+9GfevPxmV4fGa1I691v/AF6nHi8vw+M/jR1Wz2a+f6O68i9BcK+AzKc8Bl6Mf6H2qxWM6K+eoJGCR3q6kmzTv+PjdPuwC0fOMD/a/XH4V8ni+Ha9OS9l7yf9anyGO4PnKXNhZr0en+a/rRdC07rGhaRgqjuxxVSS9J4gQt/tPwPy6n9KqbQWDuzO4/ibnH09Pwp2fevTwfDNOFpYmV32W33noYDhLDULSxL5322X+b/D0FcNL/rnMn+z0X8v8aWm596M+9fSUaFOhHlpRSXkfU06UKUVCmkkui0Q6im596M+9alWHUU3PvRn3oCw6im596M+9AWHUU3PvRn3oCw6im596M+9AWHUU3PvRn3oCw6im596M+9AWHUU3PvRn3oCw6im596M+9AWHUU3PvRn3oCw6im596M+9AWHUU3PvRn3oCw6im596M+9AWHVn+Iby3sNEvbi7lWKFYmBY+pGAPqSQKvZ96z9e0u31vSp9PvS4gmxkocMCCCCPxAqoy5Wpdgscz8Nr9T4St4LXEk0UkiuBztJYnGPoQfTmuris2d99w5JznAP8z/QVzVp4XufD8CNoF3I5QfNHNj95+WAT9fzrS03xJDNMbbUENldqQpV/uk+x7fj+tcWLaxeKnWxP2m2l9nX8/mVLIXXj7dT9rFfZ/l9Y9fXVG5BBFApEMaoCcnaMZqSo45Uk3eW6ttO07TnB9Kdn3rqiklaOxCgoLlStYdRTc+9GfemOx2Xw9GU1n2gX+T0UfDo/JrX/XBf5PRVy2RjLdjPiI3yaNj/AJ4t/JK43dXXfEQYXSPeFv5JXHUqnxGkNh+6jdTKKm5Y/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qN1MoouA/dRuplFFwH7qo6tp8Oo2rxyInm4PlyMudh7H/AOtVuiplFSVmaUa06M1UpuzRi6Bpd5ps0hllgMUg+dY8jnsR7/57Vq2csrqxm3A54BXHHY/X1HapaKmnTjTjyx2NsZjKuMqutW+J76Wv9w/dRuplFXc5TtPh2+E1n3hX+T0VF8Pumrf9ck/k9FavZGMt2M+IEyyjSgnVImB/JK5HNdh44t2uBphsoZJiI2D+UhfBwnpXMDTNQPTT7z/vw/8AhUzTuaRskVs0Zq1/Zeof9A+9/wDAd/8ACj+y9Q/6B97/AOA7/wCFTZjuirmjNWv7M1D/AKB97/4Dv/hSf2ZqH/QPvf8AwHf/AAoswuitmjNWf7Nv/wDnwvP+/D/4Uf2bf/8APhef9+H/AMKLMLorZozVkaZqB/5h95/34f8Awpf7L1H/AKB97/4Dv/hRZhdFXNGatf2XqP8A0D73/wAB3/wo/svUf+gfe/8AgO/+FFmF0Vc0Zq1/Zeo/9A+9/wDAd/8ACj+y9R/6B97/AOA7/wCFFmF0Vc0Zq1/Zeo/9A+9/8B3/AMKP7L1H/oH3v/gO/wDhRZhdFXNGatf2XqP/AED73/wHf/Cj+y9R/wCgfe/+A7/4UWYXRVzRmrX9l6j/ANA+9/8AAd/8KP7L1H/oH3v/AIDv/hRZhdFXNGatf2XqP/QPvf8AwHf/AAo/svUP+gfe/wDgO/8AhRZhdFXNGatf2XqH/QPvf/Ad/wDCj+y9Q/6B97/4Dv8A4UWYXRVzRmrX9mah/wBA+9/8B3/wo/szUP8AoH3v/gO/+FFmF0Vc0Zqz/Zmof9A+9/8AAd/8KX+zNQ/6B97/AOA7/wCFFmF0Vc0Zq1/Zeo/9A+9/8B3/AMKP7L1H/oH3v/gO/wDhRZhdFXNGatf2XqP/AED73/wHf/Cj+y9R/wCgfe/+A7/4UWYXRVzRmrX9l6j/ANA+9/8AAd/8KP7L1H/oH3v/AIDv/hRZhdFXNGatf2XqP/QPvf8AwHf/AAo/svUf+gfe/wDgO/8AhRZhdFXNGatf2XqP/QPvf/Ad/wDCj+y9R/6B97/4Dv8A4UWYXRVzRmrX9mah/wBA+9/8B3/wpP7N1D/nwvP+/D/4UWYXRWzRmrP9m6h/z4Xn/fh/8KP7Nv8A/nwvP+/D/wCFFmF0Vs0Zqz/Zt/8A8+F5/wB+H/wo/s2//wCfC8/78P8A4UWYXRWzRmrI0zUD00+9/wDAd/8ACl/svUf+gfe/+A7/AOFFmF0Vc0Zq1/Zeo/8AQPvf/Ad/8KP7L1H/AKB97/4Dv/hRZhdFXNGatf2XqP8A0D73/wAB3/wo/svUf+gfe/8AgO/+FFmF0Vc0Zq1/Zeo/9A+9/wDAd/8ACj+zNQ/6B97/AOA7/wCFFmF0Vc0Zqz/Zt/8A8+F5/wB+H/wo/s2//wCfC8/78P8A4UWYXRWzRmrP9m6h/wA+F5/34f8Awo/s2/8A+fC8/wC/D/4UWYXRWzRmrP8AZuof8+F5/wB+H/wo/szUP+gfe/8AgO/+FFmF0Vs0Zq1/Zmof9A+9/wDAd/8ACj+zNQ/6B97/AOA7/wCFFmF0Vc0Zq1/Zeof9A+9/8B3/AMKP7L1D/oH3v/gO/wDhRZhdFXNGatf2XqH/AED73/wHf/Cj+y9Q/wCgfe/+A7/4UWYXR1Hw65XV/wDrkn8noqbwDa3FrHq7XVvNApiXBljKZwHzjNFaP4UZPVszfAlzL5GoQFgYrS7MEAKg7ECKQueuOT1rrl1G6UALIAP9xf8ACiihN2KmhTqd3/z1H/fC/wCFJ/aV3/z0H/fC/wCFFFPmfcysgOo3X/PQf98L/hSHULn/AJ6D/vhf8KKKOZ9xtIDqFz/z0H/fC/4Un2+5/wCeg/74X/CiinzPuCSFGoXI6SD/AL4X/CpBql5/z1H/AHwv+FFFJyfcLIP7UvP+eo/74X/Cj+1Lz/nqP++F/wAKKKXM+4JIX+1Lz/nqP++F/wAKP7UvP+eo/wC+F/wooo5n3HZB/al5/wA9R/3wv+FH9qXn/PUf98L/AIUUUcz7hZB/al5/z1H/AHwv+FH9qXn/AD1H/fC/4UUUcz7hZB/al5/z1H/fC/4Uf2nd/wDPUf8AfC/4UUUcz7g0hDqd3/z1H/fC/wCFN/tK7/56j/vhf8KKKfM+4rIT+0rv/nqP++F/wo/tG6/56D/vhf8ACiijmfcEkJ/aN1/z0H/fC/4Uf2jdf89B/wB8L/hRRRzPuFkB1C5/56D/AL4X/ClGo3Q6SD/vhf8ACiijmfcVkPGqXeP9aP8Avhf8KX+1Lz/nqP8Avhf8KKKXM+40kH9qXn/PUf8AfC/4Uf2pef8APUf98L/hRRRzPuOyE/tS8/56j/vhf8KP7Tu/+eo/74X/AAooo5n3HZB/ad3/AM9R/wB8L/hR/ad3/wA9R/3wv+FFFHM+4cqE/tS8/wCeo/74X/Cgand/89R/3wv+FFFPmfcSSGnUbo9ZB/3wv+FJ/aFz/wA9B/3wv+FFFHM+4NIPt9z/AHx/3wv+FH264/vj/vhf8KKKfM+4kkIb64/vj/vhf8KT7dcf3x/3wv8AhRRS5n3FZDk1C6U8SD/vhf8ACpf7UvMf60f98L/hRRScn3KSQf2pef8APUf98L/hR/al5/z1H/fC/wCFFFHM+4WQf2pef89R/wB8L/hR/al5/wA9R/3wv+FFFHM+4WQn9qXn/PUf98L/AIU06ldN1kH/AHwv+FFFCk+4mkM+33H98f8AfA/wpft9x/fH/fA/wooquZ9wsg+33P8AfH/fC/4Ufb7n++P++F/wooo5n3FZB/aFz/z0H/fC/wCFL/aFz/z0H/fC/wCFFFLmfcLIP7Ruv+eg/wC+F/wpDqN0P+Wg/wC+F/wooo5n3KaQf2ldf89B/wB8L/hR/aV1/wA9B/3wv+FFFHM+4mkH9pXX/PQf98L/AIUf2ldf89B/3wv+FFFHM+4rI5D4m6vfLpFjbLOVgvbsW9woVR5kZRyVJxnBwOlFFFZTbubQSsf/2Q=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result:\n",
    "![airFoilGordon.JPG](attachment:airFoilGordon.JPG)"
   ]
  }
 ],
 "metadata": {
  "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": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}