diff --git a/.gitignore b/.gitignore index d148f64f694..6ab7e7813de 100644 --- a/.gitignore +++ b/.gitignore @@ -39,6 +39,7 @@ yt/utilities/lib/bitarray.c yt/utilities/lib/bounded_priority_queue.c yt/utilities/lib/bounding_volume_hierarchy.c yt/utilities/lib/contour_finding.c +yt/utilities/lib/coordinate_utilities.c yt/utilities/lib/cykdtree/kdtree.cpp yt/utilities/lib/cykdtree/utils.cpp yt/utilities/lib/cyoctree.c diff --git a/doc/source/visualizing/CartesianCuttingPlane.ipynb b/doc/source/visualizing/CartesianCuttingPlane.ipynb new file mode 100644 index 00000000000..6e06b4af19b --- /dev/null +++ b/doc/source/visualizing/CartesianCuttingPlane.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2886b076-450c-42bb-bebd-b437fe22b41b", + "metadata": {}, + "source": [ + "# Cartesian Cutting Planes \n", + "\n", + "The standard cutting planes and slices in yt slice along index-space, but in cases when your dataset is defined in non-cartesian coordinates it is useful to sample data on a cartesian plane. This can be accomplished with a `YTCartesianCuttingPlane`, accesible from `ds.cartesian_cutting`. At present it is only implemented for spherical coordinates.\n", + "\n", + "## arbitrary grid data examples\n", + "\n", + "### full spherical domain\n", + "\n", + "We'll start off with an arbitrary uniform grid covering the full range of angular coordinates and radius in (0,1). The following loads data and adds some derived fields for convient plotting: " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9c2eb048-2c46-44bf-98b0-754f73f82a53", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:17,660 Parameters: current_time = 0.0\n", + "yt : [INFO ] 2024-03-28 15:23:17,661 Parameters: domain_dimensions = [64 64 64]\n", + "yt : [INFO ] 2024-03-28 15:23:17,661 Parameters: domain_left_edge = [0. 0. 0.]\n", + "yt : [INFO ] 2024-03-28 15:23:17,662 Parameters: domain_right_edge = [1. 3.14159265 6.28318531]\n", + "yt : [INFO ] 2024-03-28 15:23:17,662 Parameters: cosmological_simulation = 0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import yt\n", + "import unyt\n", + "from yt.utilities.lib.coordinate_utilities import spherical_to_cartesian\n", + "\n", + "bbox = np.array([[0.0, 1.0], [0, np.pi], [0, 2 * np.pi]])\n", + "\n", + "\n", + "def _get_slice_func(field_name):\n", + " def _slicing_dim(field, data):\n", + " indx_fld = field_name.split(\"_\")[-1]\n", + " d = data[(\"index\", indx_fld)].d\n", + " return unyt.unyt_array(d, \"dimensionless\")\n", + "\n", + " return _slicing_dim\n", + "\n", + "\n", + "def _z(field, data):\n", + " r = data[\"index\", \"r\"]\n", + " theta = data[\"index\", \"theta\"]\n", + " phi = data[\"index\", \"phi\"]\n", + " _, _, z = spherical_to_cartesian(r, theta, phi)\n", + " return unyt.unyt_array(z, r.units)\n", + "\n", + "\n", + "shp = (64, 64, 64)\n", + "data = {\"density\": np.random.random(shp)}\n", + "\n", + "ds = yt.load_uniform_grid(\n", + " data,\n", + " shp,\n", + " bbox=bbox,\n", + " geometry=\"spherical\",\n", + " axis_order=(\"r\", \"theta\", \"phi\"),\n", + " length_unit=\"m\",\n", + ")\n", + "\n", + "for fld in (\"theta\", \"phi\", \"r\"):\n", + " ds.add_field(\n", + " name=(\"stream\", f\"dim_{fld}\"),\n", + " function=_get_slice_func(f\"dim_{fld}\"),\n", + " sampling_type=\"cell\",\n", + " units=\"dimensionless\",\n", + " take_log=False,\n", + " )\n", + "\n", + "ds.add_field(name=(\"index\", \"z_val\"), function=_z, sampling_type=\"cell\", take_log=False)" + ] + }, + { + "cell_type": "markdown", + "id": "fe36da73-04ce-438e-80dc-975f847c02e1", + "metadata": {}, + "source": [ + "For initial comparison, let's plot a standard `SlicePlot` in the x-z plane by plotting a slice normal to $\\phi$, centered at $\\phi=0$. Remember that in yt, $\\phi$ is the azimuthal (longitudinal) angle with bounds of (0, $2\\pi$) while $\\theta$ is the co-latitude with bounds of (0, $\\pi$):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3a65cab6-13dd-4602-808f-72ea98966dd0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:17,770 xlim = 0.000000 1.000000\n", + "yt : [INFO ] 2024-03-28 15:23:17,771 ylim = -1.000000 1.000000\n", + "yt : [INFO ] 2024-03-28 15:23:17,771 Setting origin='native' for spherical geometry.\n", + "yt : [INFO ] 2024-03-28 15:23:17,774 xlim = 0.000000 1.000000\n", + "yt : [INFO ] 2024-03-28 15:23:17,774 ylim = -1.000000 1.000000\n", + "yt : [INFO ] 2024-03-28 15:23:17,777 Making a fixed resolution buffer of (('stream', 'dim_theta')) 800 by 800\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# slice in x-z plane\n", + "c = ds.domain_center.copy()\n", + "c[2] = 0.0\n", + "slc = yt.SlicePlot(ds, \"phi\", \"dim_theta\", center=c)\n", + "slc.set_cmap(\"dim_theta\", \"magma\")\n", + "slc.show()" + ] + }, + { + "cell_type": "markdown", + "id": "344f2cce-1c42-45c1-9d35-1c3d5134c4da", + "metadata": {}, + "source": [ + "because the slice is limited to $\\phi=0$, only the positive x axis is displayed.\n", + "\n", + "Now let's create sample the x-z plane with the `YTCartesianCuttingPlane`. \n", + "\n", + "So, let's set a normal vector defined as the y unit vector and center our plane at the origin. We'll also specify a north vector set to the +z unit vector. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "72bd5787-7c28-4805-837e-ae4f26f729d7", + "metadata": {}, + "outputs": [], + "source": [ + "normal = np.array([0.0, 1.0, 0.0])\n", + "center = np.array([0.0, 0.0, 0.0])\n", + "north_vector = np.array([0.0, 0.0, 1.0])\n", + "\n", + "slc = ds.cartesian_cutting(normal, center, north_vector=north_vector)" + ] + }, + { + "cell_type": "markdown", + "id": "bdabd466-78ff-4c23-a2ef-6222ee408822", + "metadata": {}, + "source": [ + "Like all yt data objects, you can extract values from the elements that are intersected by accessing fields:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "14c3e0d2-1901-44dc-b3b9-1de0ca006c97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "unyt_array([0.31444826, 0.96614026, 0.47393413, ..., 0.20902339,\n", + " 0.83379548, 0.14306183], 'code_mass/code_length**3')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slc[\"stream\", \"density\"]" + ] + }, + { + "cell_type": "markdown", + "id": "3756937b-3920-42f4-8792-37f4b29c1c76", + "metadata": {}, + "source": [ + "**At present, the `YTCartesianCutting` functionality is not integrated into yt's plotting interface, and so plotting is limited to generation of fixed resolution buffers.**\n", + "\n", + "To create a fixed resolution buffer covering $\\phi$ at 0 and $\\pi$, we'll provide a width of 2 (because our center is at the origin, our image plane will go from +/-1):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "386c6471-dc75-4e76-b23d-72663d504eb4", + "metadata": {}, + "outputs": [], + "source": [ + "frb = slc.to_frb(2.0, 400)" + ] + }, + { + "cell_type": "markdown", + "id": "5b888af7-3dc6-4d0c-aeee-9f6bbe93657d", + "metadata": {}, + "source": [ + "and now we can extract our theta values, masking out points falling outside elements of the grid:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6374e5bd-f3b6-413d-afe7-8bc6ab53393e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:19,046 Making a fixed resolution buffer of (dim_theta) 400 by 400\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "vals = frb[\"dim_theta\"]\n", + "vals[~frb.get_mask(\"dim_theta\")] = np.nan\n", + "fig = plt.figure(figsize=(12, 10))\n", + "plt.imshow(vals, extent=frb.bounds, origin=\"lower\", cmap=\"magma\")\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c3cdc10f-cde7-4c58-9bc1-5192a3a8dcc4", + "metadata": {}, + "source": [ + "but we can sample arbitrary planes too! \n", + "\n", + "The following creates a series of slices: each slice is parallel to the x-z plane, with the center shifting across the y axis:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "48eab7dd-0dac-4898-803a-7e0d69a0cf2b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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I3t2mXT/Ufvio33p5E8y+pKYjM1THJ0A6bJ2gw9BrGvKKMZA/odWUptXtby0rNC0nedj02uSZAo/8TZHz8pWXVK7swdCkvrbOuTaUPGqHJv+uDD0k6KJn2dDrt/anhZx6BJr/d0aw8Yj4NO2Sv23U1399+0A118QArwg44v4hk7QlxeFpHj2KrqD4NRp8WuM6pqG8HkZa2S4iw7pAaPjqtqPrXSGVr9dZK0Tt3teL3DagGt3CDXtfXJrokggKkQ+9ziS9YmBFzX1p9dVvvRyo+JSPjqmMiDPYhmvi1OySv02EP7o/oHZIo3aERUAzg0yKDkljdzObrkMbfpmMizfigb8ZEA35BnBog3zrYbpd3bbpaR6uzkScsb8ZkWFKyYZeThZueO3U5VFTY0jRG9hIHohXJQYCCX9QOlrDThX3hjoQwh/dH9UjP+AX9tChv2AKBtiGa8LbJf/eHqPaJf/Visxodsm/gYiu3kJ59o7whw8Aaupm0Uk/hExAXTWbyESf/S29ppheVzG/+mSjVRXzhl53RAsUy+rPPiU3oIWmDlQnZIJJc67X+RvVqcowKJ9Olzo55XbbRO6VkWs2/KnifFnxqrYjbo1tXTX3VNOFn/vy9Mc1EVBT8+ycVoy5qOa4rU4ZtjEi9l5zqSZce5f81khfDQCXXnrppmXeGcIfPqD/hK0JpIdYXQcEECpg4dB1c+b4u89k8y4NLU2fphnQD/UsTYMbY5CunCOTg3UNbMonkAiFFH/ttKGVIW4NKGKXjLRtRbEuuTdVKxtUW1WI78lUequHNE/AqR0ZlzjiNLYxIt7aLvkAXoPG1TEAM19g5vPMfP4e97jHxoXeBSIjzNQbtpqGBpAJWFXGCMOnW12aVxuKoGjSuVUH72Q1LfWn8/JkvHT7hwn5TOWz+Vn+qeXR/JaWajt7/2p1j9jcj7F7avn189EdN31KR8ZM8//OCLYxIj41u+RvgvDmBwFHzqgzEAACzrW9z3ZSJyY4TadmVDxJB5lr5GOFU6NSzujR6TmdUzElllkbMm8YEX0tjOi0emRzHxmxahrQj5KJY1mm3g9teQd0KWP7dbOsOr9zlMaE8OYHofqW9zqVPEAUH3EWGxvi07RL/roIb35QO+pB05kkxlf7hZdVGy0hQurTd2Eslszu28B/8VsKXc/ma15PfgotEDqvku40VgacTpfy2HLn0OXvpOl2FF5pA9uWkS7FI+k6H00b6LH1RUwP1VBW81p6rbdsE6NLbXiifnFwG6tMjQxwiozx2RrhzsVWFnScll3y10FnhIH+M3OhOrJO8/zFQGOkrb8YaGJNzwV0k3ldpqoA0pFTvmir09LE36nLafVYv7HVEbBZJ8v5pfUXhi6rJzNWl1Tdcu0mI2RSLx8rW3E7ssVQZ03xCNv6i0Wf6A8qrZU7Nca4jIiTKCvrNkBkhCN/oDHGnf8R6NwLORltaHUH7wwu/OOQKoqvuYp1erKWB20ZszDplWmDbcFOds0uF4YGWFY2ivGzPJ7RFFdPt5rR8C4Nv9YJ9G4ISydDB+I05SY5dGPM3PwV+CiGeE2E1z8YqFojFo2glDGQNBWuBGAYJKtl9Oisoxv/cl01T3Xkj6YhTUT1tchqpEaXuVGuTt9kH+IcrN45ZbLykc+3dVvIyzEaNSs92g1E3K/AE1kxsLVyVZC6552rArFfWdOBRo+4JDyZCsCqQnj9g1E99l3p+p90FNdEEsUQr4Fw3YObH7XprN2n60jaSo9CteEggKj3g6b4WfFqWHmGry9YHqVDbLTVDcOTSrf61kFSh1NeLx0Y1okQ11n7c+sq5tHX3f1RuiNDTl1yQ8ewXS094ud+VD1Ia69VWrjuwaged6DGuLgmktjKXhNnCZ0RBpoHa1XFIxkeSZMJt2XVd2JtuFeVz6/3SxD9jPjP4xvbe0F4JF0g196eEjpvb18KW665f7n8bb29/K08DK9X/hyPztvj0wZW30PvPnj8NQ3veyoN5hksOBUoI+IZGBhh+dRaLprIBz369dLsZI9ELFj6Ss+yY+jnzNFEr3et+VI8emLM5hU1RkKf1iEYW/Oc49cTWamyeBNpuUk6zZ/i0XXMTZSm8tf5dCNxwy/0ulLuDrS/kU7DAY6MGeDimkiiGOKJCK99KIAj4MgcDywdcNUaXOu3lTTiJj3y6VYJOiV00dDVoWlJvhG5nIwN/dLpGtFmQ6ZMczqg/iRP5pWory5r5KM11157pGQ0j66n5ZN7JaFqmr5c9L9T9Mj3jFiXTQOA1QLhtQ9F9R1vn962ewXF7VgQoRjiCQivfWh/5PrFBUgWZ5hREC+r5iTfRYjSuJ11J14MRl1ct3SR0bq8kDZvRDZl5DZlxJcLU0tN6KXScyFpY/BGvLlyemFrGmMhe7nQNE/O5aPmXupIj4qB1aLZXMhGgHDVbwRUGWNsZYwx5mX/pjooY1xGxEkUH/EIIiPMTefli4t+plwMZzuK4Vp1lM4I92mio5NBY4z1ZjAdfRXTOCDS0dFWVUN3+Lju+bxrrUegdUT8Oi2RPuBZ42+sHIOyjJRj0F514trWtaamba2emqJ8wA2fvncyotX3JXqGavMcqBewLp9+xlBXzbNnnqHma+3kg5lm/41hwl7ojyKiW9R+6M+dKnucKCPiDOrfVg94NNqkZgMTCVfS6e2nMrOfxoHilXTt53CSDgxcFOyFqTGBdbiWw8d1rIuXKk8r72DT9KmYoifHE6URNwZNfK1C06NmuVYuFXZC3zgo90XFcXuKS0XyA1RYW4reuykaY6wm5WT0KyFyNk3K1crWv/1QLP7uCR4ZM4ZzFRtiyl7oLf4jMz9+TdljQTHECdSv+iYgHDWGShtI/Wm6rIaTdJKmV13ZNPEnT6Frn6NHk2tgOCmmw6Y6WBdC4qNIPok3AcfGbm2eOfkNdMQv0AENiNvITuIB/T2RrxbtKhjQ0PNKnbJ0Zci9yTxJkxeJToPSWVeoX/VNWHzX2yY317Fj+66JuXuhb0t26yiGOAUJMVo1DznO1XFn6Sbi2hhfbyEGU59m5cIiGo0O6HaSTo9uNU2X15u4s3I2PXetR5GpNpp6EOaUTpjjGctLGy6PP3XdbYeZ4Rfj2Y1IEcsJDWhGffrlppdHW7rwdy9uR0anaYMuPuiLR4jC3U4wdrANprcX+sMdvkcQ0TvR7Ar5j5n5vTNkjwXFEDuoX/mw7uGWh6eZULOj2/YfmVizCydG0powNZWx0MUA2rhVvYhCaAvpsIjL1lXGyOkOn7v29DVzUjFsuoDM9RRYmbG8bNvpshPi9kte0zDd6qt4uHDDLh6JRshI8zr8zSQfx+VoR+l+WvvCuVj1z2fLU7/yYVhc+dZEA+8Ryq89E5eYsywvMPOF9ren0D51bwfw1cz8OSJ6HIDXArhsouyxoRhig/qV7nbI4LoCAjcPvDIY0hG4jf2lBQ/T2mD+QVr7SUtqJCZ0Uu6QJA0Agj6OR3XKGddCm8LvyWodfQHVz5GRUCdnukHkxzW8XlnEGOlrW8dUvZLXjChKIbpXrYuoo9UU01L3ujWqWm9XH53W+YCNMa4p0mlxUo3xmnHENzPz+UTa6F7ozPxZ9fs6IvqXRHTJFNnjRDHEBrxcgNpY4a4Td5+7bchRFeJOLm97bibIyE66yWSPO4FXdTJd5wLAqwVQhYiXtX9RPdSDGXwTejV2HenQ6V2+/XUkq+shfGtgkhwpI+lEGwyuWwO28TUyebRpXduFqm+n0L4ItA/ZRkJon7BqUx0to8vS6CN023DKPTLGmlf6FNoThO27Jkb3QieirwLwF8zMRPQwNN8anwLwl2Oyx4liiFus/vUj2o5H4ItHjTHVBq/r/NT7cSU9MkYVuK56Y6zlQEMj3f7LsuhD61otBnoi6LI5URSD8mvj66Wn9Hv5ebBlSBjsgYzH49XHw5Qy2vvk3LeBjNemnlz3gqpaQ6jvX5XUJy9VLcMyoha6LhNx92xFctoYq/Tly/4XUMU4+u4/9tttH9jyZN3EvdD/PoAfIKIVgM8DuJKZGYAru9UCzkAxxC3s6IcvNj5hUhNngxGr51ZodWgjqj+PrZwdhVl618E1b8sXldnSAFCFmdda3naaKZ1oGzLbyc/WJX+daAs9mtU8kZuhdxPIKBhALwu4vHqE36WpLx7teunSzMhcG2NeLdDtfax0nBTsahvMsb3QmflfAPgXU2X3hWKI0YyGoU8QkEGrLJRYmCdIr44iHm4E3y04qBJpbbB6FE2RoNcTaeI2UGWMeKZejyFg/8uAJpSh+YRHH8OrDwoduxb5ThkNecTNwJSmIUePjXG0GrCi2JDK4g/5epAyduk6hK7PE8RY/etHnJxR8Ql6MZw0FEOM/jMRwGA5LNcViHlojJUsie9XGwcxat6G70ztvrdAvEm7omt0s/eK19IkP70/8tQICVv3MXh8+rN/GxjTN6usFBuqsesKKkqC+2uPJ9KjdIms5Y3oytB24sol0d7XaHRs26emaFRtjfG6fvvtY9pKubOKM2+IL1743wEsep8wELsi0BrqGi5Px8fVwK+sRyxkNgvqfMYqzrd3NcS6Il6dp0cbRBPE12RGU8NrP5IgzndI7xnI/SRO8Y9HX8TpU8vlX09si6WSbV+OlmdAU75i7cpo4o0pLo8ymN3cgE4LQ11dW4ihTbkyHJ6LF/533Onqtwza+dhRDHESW/nIPNT13o0Rbj7Jw2qBsDwaPizqOqwW0YSI9StLupfGiTSuqybN8rf5dD5F4Q00lA/k8nU8SmaQNnYdKC5Lgu6VLWrGQO6f19Zdutat20J4MvTRa5WPy6vabSDv8annIuKV++683LvoBictZNJ4tejTnbYGE8LyqHke2zR51vcGTj8Dub+zgo1HxIe+3rv7XGpverh4BFQMqoLL13UeO8LlZjTKYQEWeZ0GNMH7Kk3nHcUIt7pQMUiHNbUdX2idvEMjJ11fx5WL08no0Xl7MuSle/lMgZER3UTc+NwND8lIVLWN2wZWt9MmkazWq8oCpZPU19HgnqkXqqzQIwAgboxoy6vzitKAZpdUE6LWPX8Vd+3SVSlQ00bagDl8BScP23BNHOR672iEYEejywrNeXQh7qx6RLg8ambDF2Ho+5LRkd2jgqn/TDV7UHSfqHpFXSCw3WuXqZkg1DQg5nN4BnqsvNpvQfOytxew5lP/Rjy6vabCkXPzcMogdO3v13WycpZ3VFZo2h1g27QteyfbuQmoM4h6lz1Zptw9P2Jk7csXiOWMzu4l1X4ddG4bxQdg/y6K4ppIYhuuCW/N9r0dvkcQ0TuJ6PVE9KCZslvF7b/8txAuHmUnDzgQ+OKiMZCt77VJUJ9Oq8pN7+SXi34/Avv5q/cp0KMnbRzEVbCMP1ObF8FiwBfthSGdXqfrT2cvTUa12mBofTm6lbX6pvzl5KaUwalDFPZlfrtp8mdcEgAGbS40y9fp1vcs9OWJ3DdCXw7dU91zVVftdpyxK0XSebVontXMi4+ZEC4e4fZf/ltJnl1jF9tgnhZsY0TstZb9Dtp4vTcRXQ3gagC49NJL1y5sl0kg8B1HoMqJFValCKuq+awjf8KpmZWmfhN3o4PbvR4Gk0uG3nSwmB7pWTV7Vghfo0MbbXQxxx0PAbqJZcKqh01LwUljQO9hYeNyI76EirH0QVwze3zjnXW83uqet/eZ2z0jbDq3kSZUcb9gg9BN/kX3J/QTaPE9UWUze1NEk26Q58urVK+zcz2wo4M4MvJ7c1HIS6fAxTZGxJPWezPz59rf1wE4N3e9NzNfYObzzHz+Hve4x9qFvf2X/1bkauBVhdA+qNaQdH5hGZGo2e+us7Vv7nBxgbCq/PRAA9mIrg6Z1HR9IGYzoVgN+OyBmxGPTuf42hoMy+v9efl1kzD2gFHNh4zeTHrXZok8p5Q3anOv/vraacNBuuLp/ctxeTW937Re/NiqzR1+SQurCuHiIh4ZWtmVmjg2o0epe1gumheGuHKY9jYqlkUdc/7OCrYxIj789d511W3yHS1BNtB+XNd3XFfgYNK7DlBFsiLHEndq/Mmd/1HRADT+xxSfw6Mni+x1lOblqfO2MiHmHbTFXD+x5U/pVnnrCTK3zK0eDuTKWL3RCzrRroP7WiXy1C6PbiFG24ZSHvEha902beALHspGEGNtfcp7BmP4sijosbEhPqT13p9/0beg+za0/tnWN8ihaqIarGHoXBeyuioMJ2paPl5VfbpJQ01gc6Yd0Bj5KFKj1ZOldZ1ddco2D7sSz7126hctLrE6BfqkCa1Xd/hoBDnSAclpR+0LVZED0R69tjy2zDrN6hxrI52XyofRPiPEUbmzNFV2rtp77RjHxn9cJd0Hkg5goFe/ZLrokgzf51/0LfjCH3qzm8/OUFwTSWxlQcehrPcOd5wDLcIgNC1makLQAAwWYWhIGNFgEYcyrG74GQDIpkKGHm3yI2mq43V8qrMmQ9gS125Ymtav5UyH9sLftB7S5ZkDR6YLjTPhaR4PVB06XknXhsvW16YZIxeFrhlasr7KnRWFjgn94sKni/vAphHHk3/ey12MvI4tTi24aQ11NL9wHOAyIs7hzKysu+0Xvh0Adf5eqtIuhq6zLY+aDpHhk9FNFMYW+s7IAKqjepAWZFSkXQNMw1MxRJf5VO7CtiTMTDpkJmRtEJaWCAMbhKN5NCe8rftM1yOfMReFl65Hd4mwNa9MuesoHG0kVE2uO4PohLoBGIa/adcGE0iHp2l9xuDK4gwvjdWiDDe9fWYGL42BS6NqjTB1b8/bfuHb8UX/+A04NhRDnMSZMMS3/cK3DyY8gjLG7uhBvnC1H1IbLj0hw80oOtqJTaWH5aKZaTejMglFGoyaVyqagqkrSzSqVbRItjYjX7uZT534bRC9eDx4slNp68qM6MqWOVNv3UZExkiLG0TamxS/oQGIDB2A/iXJDZ82xs1EXK8jMtQAgvoiGaS3L4doEjFRdb3qMdp8nukYjfHZCkebizNhiFPoYnqpOVnD68h6aalsZ+idKBE96N33eqyj2emrT+t0Q8mx0aXLorc5VLTBVpyKJ9TxNpqDEDyK6+HpOBRIJMnUOgpf1EaOjuiaHR5LE0OnozaA7h6LYe5CyoxxHKS1eXRRdIH6zePbl7jVIS8Utju37RPFR5zEmTDEXvhP80NGps3JGzLZMjgOSPlGQztZltqHuI87Hu4ZLDGhg8gMSVN5A/2nb3d6h9CMbtmIXkbY9hQNvXnMYCMZU44B31gnlnQ9ybUNaH1zyiCRBTkZh2/4TKh4YlInZBiZjqcta08jv03YuDQcmW7kq9JENtgJPVtPeZFzFY3ybZkjw38cYJypcLS5OPWG+NZ/+ngAR90EXTY8rf0U5BoNv+nIfVzxYshjXQRMbWce7jnRzZrbST6m5mm1E0MSUaFeHPqp7nzL6pp0GuKvVjvZF83S68kuhz/Kw9Tdy1fD05HisZNZkYHyyiHlTpUrodeb2Oz0tfs+d3WqKZZX/t2uvWQk6hlBz5fr6OkrF78wWB2RZNtiwNPypdBvfFTh1n/6eNz1p343ybsNMI7R6B8gTr0hBuQTsZ0Qac8VG0QdJPgrOb9OGYSBTrsnhfEB6/ysUaYqRHLdb8tfLyJaxCfXUcesIj0cqqG8lc3ojKIWbNvpl1DUkKZMuY4oRifXFrlyKFpUd2k/Vc5Om66fMXKw/F4djdFr5hOGtMZNsIjoUX3CcI8IPcmmR9DRijn1UoheCNalZV5WYRn7nZJbmm4bxRAncaoN8a3/9PH953tn0PqHQYxsk6CMp+Kv28iJ1EjaRk5YPUHcC9bt0MWaLgZ06TjDE5O9U5QVTfIyS4P7a3+vYu93DzWCc9KF5svCuc4h5u116/qqNDcEiwbp9nczchS+WLe+tu3qyVtadO84fgY0r9DjcLp4aXKwERMONE9KD9A8p96oW/h3Pyoerlwt6HGqDXES7TNdy14TFadHBd1ETBP5UGl/bTdyinm80bY3ARdNzBAGn5pRJ1UvkkH0hY6U4H4E1IdT+S8ie53a10B06XRLs7KjUReZfGx5Ot+6aYdcPql6DepoIiaC7P1AcVqXl7ovkQyAwYRaez8axl5e0oUOqHtm5VoenU5V68owPB4GERMZ3p2Ci2sih1NtiN03MA95ulOXKdOxW111vYgjI7z8dHSEytONrPDkhL8e0rmNre02GRJDrgeH8tDT8Pc6cNtjpFOt0+nW0blR56bYINr9JKJIiNYYdi8AoQU9um4mwRjqBSt0Kaula+Ptpdu6crsXhlQh4YaQF4o7Oavy7EjHMVothjiJU2uIP/NPnoB4ks48sHaWvP1MtJETA7CaxFHRE150RJNvSKYPVtGp2XcbPTHgXcUdzIumsOWO/M5jv7cFx887K32TfNesrxcd4fI7hq6Ts3ViIBUtMUjz0oNaWm11t/olf4mYsJEVXmifGHUOFT7zT56AL/3Z1wzaY1soI+I0Tq0hBmQE0/rFaqBa1EkD233ycjvzXAOVjZxQPAD6sDdnybQbIWHLNjF6AkC/+spETwwiHvRnePuycPkjt4lPj/SYCaaUzzKJsU44sZPOKgubKIcUvZ0U61w+9gRnnY/y3wJIRkVk6bqIXiSFTg9xJMSw3P1LItgl25m24ZoQ6thnPPueFmwNp9IQN6Nhg0AI4ahbRVcl9pvQnSKs4qiIFC8vj7pl0JEfU422U+nyu1LRE5o+9AcvIt5gjLHtfKFeDHRYvhxdftt85Nrr7Kmyp2D5UiFq1vetr1PlnlRnVRfL5+VvDZjo0PXxeKM6hNYAt6vkpqbHmbbGt3NtZQxwW7d6uYC7BL3FLkfFPPXk7TOIU2mIAdP51YMnn4B13RqWKhHKFhyDnImeQCAEHupMpdt86vpoqDv422GGsEhvXynXgLvd5azfWhfg78kADD+rq3x7Wl5rqD19YpQGe2x45VP8g99q8xyXx9TNbndpN9+J5Ey9g95lTdUzcoMZNF9x6XTLAygDrYy2fUl1S61b5GR2Asbkr56ziANcyJrHp3/y7yVHYBG9dUFwe3pzajeqTqblF94+IsFxV8gpu4H8dNlNyxin6CRgJdfxGpreFDyShTIQbQfU+dnfnR45ykd19GiUruqt87Sjzo6u/5j8vwSvqy9XFl1mp07db3XfuuswNFSDPG3ZRMZ7Fsy9iPJTW1laXWG1aE/0dtJVvcPyqF3UEz9bFh2v6DTtmsKnf/LvJdPWBSNx/0f+xjDhBPknE9G72r//REQPUWkfJqJ3tyfLX7/lKs/CqRsR1+25YtWinXQbecPLAjWuG78wEbJyzD0v0EzsuCMbiW5wNxSiRo9Nl5HUcjGgdw+l8eUy09DH3F4PRpqZjug+9BP0uDTrk52DRJ6pvKN2mVjfnA5mivUQw65Wc3mk/ZcVuhOkNF0iIUy1xtKFh+0ijETzDjYCGuEHoDYmItRjmzRtgCmGdQ4mngL/3wD8TWb+DBE9FsAFAA9X6d/MzDdvtWBr4NQZYoEO8QFxZ5gBuEai34AHkNiexsim89ChS5U+s057Elb9QgLR1Rl/Fc6WSqsWHNGowoB3UB+nfkRxuSZh3Y6zSYc7xjwHbeLpiD7n0b1ELS2ozZeYY14djpZLs7IAUGdC1XT5gnoWozpaGXlRo/k6C3pPDGD70Ss27+1i9BR4Zv5Piv9P0BzHduJwqgzxJ3/su1w6hwq12dQna5jk4V71E1TDDdvJ8JLL1/G3exN7oXFeWrcyb1UN6Kw6U1eG3DUwCCHWIVp6U6HTBr1xjw3tm2RytOEy11Rxb8hMeFhIbfozkgaor66oIrH+6EsOiP27CZ3yzMsxSqnJUaDpS/f4569Kt8tcqJfOFuGdAv/wBC8AfA+A18elwhupaYBfZeYL2y7gVJwqQ+xBfw51+7vW/cM3CD1LfLYGWeo8Em3R8emQNe024HYzoETYmk2L6GOha0zQxyh11/JbRwJEq83GpwqmTuRsY8Jnm3l19eQ4tC+SN+Frg2t1j7wQNgDuhj9AOoxtkNa+FNncKy+crfk3M6EXjXp7fqlDlP/EdtwG1nRNXGL8txeUwfQUuhUhom9GY4j/V0V+JDN/jIi+AsCbiOgDzPyWdQq5KU6nIZ5ww7sHU2Ivq/gIJc+nyEzg1VHPn5vVVqMNLxwrlW47YkSv47PObNwoEIer2WsdgjbW+XS6nYFPhZfpa/2vIBXm5qVrHbn8tlInG77mhLNJ+TSPLnP3wnRGmZJHsh1CNWiLYeEp1uPUeXAfQhXlq+Vy+ezMPbGe2puZ+XwibdIp8ET0YAC/BuCxzPyprjjMH2v//QQRvQaNq2MvhngrURMnYebyf/zwk1FfPIf64jmEKQ92C/HFhtWika0X8ckIEbMaXa8WqJdHEX+UZzeSrTq+VLqkdektvfvz6PpFYWjym1Ue+sXj8XW/nXSpW6TD5mnKpsvclVON+AZtodNzur16eGXTddLpDq8eiVo+u5hCy7hlM3UJtYpYMG0R6uYZSqUDzYumXh41z5DetCfxfIf2eaovnmuiMBK+Yw/MTVyy9KP/8cNPniQ3DTuJmuhOkCeiO6E5Bf51Ua5ElwL4bQDfzcx/quh3IaK7ym8A3wbgPVus8CxsPCI+KTOX0QTZsh8FRpN0HbN/gxv/GUF/8Xjy+sEOLT+RmhiJRiXU8RFRs8zf24WNjY4on/yuaz194k5szq5kY+lju6t5G9pbnjzUJ3O3EXrKiEiefXuk6zWWPkzT+erd0lI7rqXK3U28mbTQ+XVbv61deixGuK6657qboHWWKXeTbraV3A935bZQL0uJltiVm4J1ftvSOe0E+ecC+HIA/5KaBlm1I+yvBPCalnYE4Fpm/v2tFnAGtuGaOJEzl3LTawn7EcOc22nNk19pf11i5hrNrLXEezbuBk9nqzdQxNNNvHA/QThI63YbU7oUTfONXXflcTqvl04Et6MLUulzIjVSZczp0HVbp15u2UMcFWGvpf1tfcWgyj3u3QXDtIY/4fs1I8Gk+0vFPU/hj2TrqnlegX5Qsmsf8W4m6zDhBPnvBfC9jtyHADzE0veFbRjiEzVzmXzrtg943bqBxR9cOYbVfjZ3DzzQxA+PxCdzqHp3WIK341HpkUsgldaNkOIYVb3BfLOtpvZRJos6CWPyXpnm5mt5e5/0ZmWbm69uu2aJchVfB2uAxVdNkVGL/diUNHidcdbPXCLqofmXotNYrExqRFurzX1s2Yd57Wjybssj4tOEbRhi9wPIZdxg5pKIrgZwNQBceumlUdrHf2i+L4vbUxEC0C/KGDOw2lfblcu4Lqyfr+Ud8OlyJNLdNGcUBKhJOcN3nEi9BHMdO5W27c/Y2Wj9pV1kgy1TF5WQ2dAnlybPhjeate6DhL907FmVUXNqW80p+PgPPRn3fNFvzpZLlanAxzYM8bHMXLYj5QsAcP78+eiJqi+eQ7NoQ4a748Yoiidttw4UVEf16KigXwASP+TevhWyyk7grcYbnOLgpA19g3HezM0SWOs7Tu44F/qTNbQPOkXL6RjLL9cJbdpUnTZtan1G66DaMGnIHHr3RZK5lzIA0NDypAy8lM3jHVaij2ipl6pb07R29HSFOl4WvRmmLVk+q9iGIe5mLgF8FM3M5ZM0Q27mEkDFzLeqmcufmVsAGTXUJqRIL67IfmqZB0Qe5NHFHEo2OlhSyQz8YtzEs4rrwStL58O0o6i6StOJ89ciZz6R9SnFVt6+rKLQJj1q8/bkNTzJDu3oi3RMyX+s7EIz+wFHrgTrWnDqYBfxJOlOWrNZvO9+iBaCtJERg4GA0ucZ/qSf2LSrRfSVpxaHbB3cu1cKhtjYEJ+0mUs9IYLQhIVVerQ64vuKfZytj1IOCZX+l1jUoeWbyA2Vp3VZAJBlrh2f1eN81lp/oh1Bpa6FptPd39xvwxjpc+gRj/l89xZDeIio6hPciyn28rdfIB5dymdHnLqMUb6Z667cCZeFV28dPWFl5dkIq2rAl0JvrLXO/nfWFaReGkGe1TDkn1KOOWDsyMCfEmxlQcdJn7nsJ1j03g7Ng+dN1vXlS+gJaltMGk5SafmmI6ql0s6zyBwvTIg7lTYyw7JJ2JstYze7H9Lpkha9vJrUzihY/j5/n+7rS8P3fY7rlHrr8jVtQpProydAdXtJuuTjGbwp9Jz/u+ep0kZTXn7K3xu3k35ZZYwnU1cv0bWOv7hgdzj4lXUf+f5ndL+nfvpon21wdlFL7YCV0gG0h4o6HU+HGFmfort6TnWS4Yq7nleP+HTUgkfrjQsb4+NHAfSdVpcNAz4vqsGWMRqpOobES8/pTNF1CKAXvTGsa5x3bPh5YMyjCAhTB8kjjmKx9zZ+BoCEb9jUtTYnL9vDZ31DL8/A9MUcng7pBx/5/mfgvr/6G2vpiXBK9zPZBg7eENerKmnYRqE7q/htFcTPO6qX+6PKhTPnV+72FQDgbgLUGcLhhj8djxOsMkaz6bIN55heD1P4cnlPSV8XVlezOU+V5PHaZUyn6O2MXcIXG6UDSdeYN9HXZzTNnRbtOOhhZv/oXshbMqDFNZHGwRviUC/6vYHVg6b9wZ4h8zAY9a4W0agj5xu2engVH5GTDHGTc+8kXfyHyteod4Hz/M2bxHx6ix9OGzatoztxJpOuNq29d3biyw1NkxdyxuUwKEuInx23fCM6RE+0ox+GZdmq4ZzhrjqLOGhD/KGnXx1ds/NQA/FDmTqrLod+xNFHU/RuDN8VkSuTVwbL57otnIksnebR5xrp7ERP9Antn+u2TeTy2HZ+Y+0XhyM6G/4AA0OWKp935l22LZXe3HMwVp9or5TQH24w1meApq997Us3WWtVwtdyOGhDbJG60XoUUQc1UlUugTmLCrrOoAdJCb+up682B0p6eYfMCqhUmtdJPSOdMmh6csirs5W3ur3d4MZeLql6CM3Lw6N75c3VMcXntV+qrLk0WxZvI6mc8Q1eHHHGjqVeJPELJKEgMSeyixdrgY9TZYhdOM8SZ3xfVHH3YM55EPVpG91nX05PN7mkJv2859R+0lEibYTe1VVfO2ndC4Yw/FfJJWWcvCTN7YhsdEwpo6XnyuqU0eZlX6pRWQ0dcPhtmlTNM3wJoyf8UxagpNAZe84YXWCoV7XZTlEMcRIHbYh159r07d1PlvgPS6Un33IjE2Xk7egqt7LLQ2oSz0uPNrLJ0Kf4Sz0+bRy6zYASv+fk5eXh6dBpQrf8m+YlmNKWNi1n/CyfICT8yKN1Ui9xOwk5yHsb/WILhro5FmozHacZB22IVxfjFXCCqgpbiZPUo5vaTL7Jp9uUB93Oaqf8yzbP6Kgm59M93kVMfV4nDOCYYew++TO7l/U7ssWRCIPTL9aYIMvp8H7n8ki5WKbkJfIAXEOnDaZ2Twwmi0W3iuHV/06ZUOt+K1dMql4bGV0x7mHoRtmWS6G4JtI4aEMssA+4fZDs5Ng6E3Y6L883COQNbErHmKydpLF8qbJYjBmmKTrW5d0HppQv1yap+yP3YcyH6h4WgPyzYe9lzle/idG1scmpgxCKj/j4cCoM8RiCOjkBMLPWZlQ7dZRr4RlX0ef9HpMfM7h6JV5KnzeRt+3OdagYi5Lw+FMTfV5EAzBuyLQB3OYodzBJx5T2zx7b81CiJnI4WEP8wat+cDuKnBntyPXQjp7HOqmn16ZF0iOjZ+k8nswg32gyLx21MZDZVidM6ZqTxzZ0zMxjtH0EKZmR++PJDM6Qs5j4crAvBn2Y6JxR+Bxs2ueKIU7jYA2xxTZvsh7hhKVdYgpz3Z+a3BDGPz01fzQpPyY7oYOlNv7WnVQvLrDhW1pu7OVjY119xrRv0ytPSsec8mTrkfkySI5mM66CXBskzz70ZAeF6Z8nZhoYcDvxJc/ltvvB1r6gGOn7W3C4hrheDoPiAaA6Cju94boDMBNggvP1g1stYsMwqi8xNW2Nv2BwXE+VT0dFAzpV7Xl6VVMWubZl0undv2KggjIEikfn0RgTU4FEeawOXS9bVq/9OnpFbp1S5dH5jKXn+DykZKPYYXVe4qjR3xa8Z5N4fLn03GxQRsQ5HKwhTs3s1s6qpdTknBjKbSIaTduObDrSlIkbAOA6lkn5NzWfzcNLbyIfJAJhmFekX6WLXOQfVcvMg4qsSJUrVS+tX/Ppetsy58pq5b18gfH2Axrjvm7kgshGtGMytt28QiLKJDdy32Z5iiFO42AN8RykHjRrKIHmYVks6gFt3Uk8q0f/azt0pc6dy/kFtQ6LOdEU63aMKbP6c/WPyU2NDpmiP0ef0n65e6RXF9oVkNuIeLDl6F4M6pGt68VODPpGKHHEWZwJQ5xCqnN6o+oUNjHQNv/gnHEm15WaNPTSUzoL5mHqC04Mq13WnTK2U+9LyvDPva/H5tqYjBI1kcNBGuLP/9ePAJeO820VmRGV+3gTD2Ucoz1l4ggYhuCxnuA6aaOf0wamuL2BwVFXY18eXiih93zMnt845nv/wLt/xTcea4ZnBAdpiMGEetUXvVoknIUY//Scmt9cuGFKOaOt2fSoKtU5Nc1xeWhd+/5MnRV9sQd4hhLIR0REtPYepVxOyZe1o292W8x4EXuRJYLc/Yni7jcY1ZYRcRoHaYiZ46WnY+vtNarFNEcVB2p4j8FINDPvzidpMorCGG57CrHjrkjtq3BckDJ4J0MfdwdNnZIx4DNl9uRd/dhDW3Oz78RYfpI6p89E8mtWp0RN5LEVQ0xEjwHwS2gOD/01Zn6+Sac2/XEAbgPwdGZ++xTZqZj6gNQzwnJCmO4rBvLn33lYt5N655el9l1wfZWJSUrLb0dQXsRGLjbYk7EnlaR4cyM2V++UUa1XL7MPhAdvT491MGWDIm3E7bFMk7Dl44i2eYjoLgzxSbA928DGhpiIFgB+BcCjAdwE4G1E9Dpmfp9ieyyAy9q/hwN4MYCHT5Q9GHiHa6ZAxLM6zZQO0Z9Tp2kUpeXKJgdqWl57eKeW7w/ttPlZ3uH5dim9QyM8NEqe3t74yvV4tETqMNd0mi2Hq75Hstw+9u2m2Rky92JdnCbbs40R8cMA3NieyAwieiWAKwDoCl0B4OXMzAD+hIjuTkT3BHC/CbKnEvNnwXfDC/SdPzUCy+mbfGCrw7eJ7BS9U+YDtlG3ginYSdTEqbE92zDE9wbwEXV9E5o3zxjPvSfKuthHJzkEH9c6ZTyEeq2DTeq1r5HpnDIfRxm32s+2f4rzXmxPCkT0ZgAvYObrFO0CM1+dEQOwHUPsta69fSmeKbKNAqKrAVwNAF9157thuTw3p4wb4xA+GQ+hjIeCdWJ394Fjn+g8/qiJS4joenV9gZnl8LxjsT0z8DUAfpyIvomZ/6+Wdn6K4DYM8U0A7quu7wPgYxN57jRBFgDQNv4FAHjA3e7JudMqdoG5D9E+jOKmq85OmyE/xHqdNOO/7gkoAz3r+4hvZuaUMTsW2zMDfwngWwC8iIh+B8BTpgpuwxC/DcBlRPQ1AD4K4EoATzI8rwPwrNYP83AAtzDzx4nokxNkTyVm+4hHOkQUvpY6i28EEtI0Fh43NW2MbxPZMb1APjTN8mroELsxzB0AHNJLYdvYgTvxpNkeYuYVgH9IRE8H8IcAvnSK4MaGmJlXRPQsAG9AEwbyEmZ+LxE9s02/BsB1aMJHbkQTQvKMnOymZdoX5sZmxtEHm3XolNHoIhHMRjwpXczk8yY2vCGKz37LhaO5IXYhLROx1cN9G7TeXLlSIXbJNsgY4Klhgx7/3Hs8Neb9ULDt0f4JtD3XqLK9lIjeDWDSJs7EBzg1/IC73ZN/45u+Z7YcVTw4Jsbl66II1t8KcFPDmsMUw5LTf5yfv3azm7Hfx1UmwZR2m7oKzcM29oiYCrsfSQ6Lo3ott8Mz3vbreP9nPz5b8AF3u9daffYR/+//fUPGNXFqcKAr6+JTNXKb9KQ2X5nTmXb9Oaknhrw9Ccbyt7KDkeEeJ56ikatZ9ivl2+SFt26ZNLzRtPClVtzZa7v50yYj4bmQvKZsViW8cw4I1bsR7mGy7kzgIA0xAKzMyDa14CEX0D+1g8zhFXhnynkdO2UErJxN80aTU4zGPpCP4z155cvdo5SxTb1orLymTd0Aak7Zp/JOlWMe9rV1sMFk3ZnAQRriL778XseeZ86gp707voy38i2Vn15sQaQ7kugqD/euoe+xvQeVmRCcspNeb5S8e+dMHtLJmeT74Oc+fsO6stuKwDiNOEhDvC2kfH+1mRgS3jkhc+t0nMbY2rLpPBORA84S45PScQ8NY+1n27q2x1Wp+zd37xGd/zBP//nz3FGLxXRf8XHipJXnJOFMGOLU0t0pUQ7bNGi9C6G9zuy45hn56NNy5KXQ5RXS+jbFWEja1PC0KXJzdE0tq9CB8fYTjJVBP1P1iOymzxYH6p4X/WyknutUFMY6L4z5OIwFMvvCwRri1MO2zsTPVo2thExheHrDnLxtSNiYrLcjWyo9t5NZyk/p8QHoQsc0T6h7/3gqX10vyzvQYfJap6xeONuc9hM5L1xtyvOTDN9T5dFnK27zxdlN5iV8vd5u3qlzHgt2g4M1xKtVXPSxzrAtYxuNSmsaTMqNTaxl9WVGDJ6s99LRfN6ZaTZKwYta0C8Qr3wDY2x4vDxSE4le/qnJR81rebxwOB114pUn++IaSc/x5fi9egqio7JCXBZrGHfxPGtIBMbWRrFlsi6LgzXEFuvMOI/panyAzQO5jVjXlNFNGeoxw+Dxpb4IUoYr9yKYGu2QMtg2j1xUR6p9x6JCvPKk6jEWymfrZOlTRswa3hfRnEgd/duGptmvhaoK2a+EdbBNw8lb1nfacLCG+Fvf+hz8h4f93MZ6mOOYZN1R54xqU50yZSCmjJbsCFQjZ6C8CZwxmU0wxbAdh445eeQWaqSMtffCsfdm7KvHjsxzclZWw75w63oRPaNW9zaM87e+9TkA/eTa8sUQp3GwhngOUp/eGhtPnIyM/Mby0J/SHr91B+T8zzk3QEEDz4hOGXELv/dS8+5drs1TXy9TZHMYexatm+O4/MHFEKdxKgyxfehCqLIxixvNvqsOnPvMBfKjnNyoy/Lqfy2P53LIlT2H02Ssp9ZlbCTuTthNMLi5l/3Y6Df1XGzL9WAn7Wr011TxjvzRJWoih4M2xMuL5yKD6D0wmzxE0SdlXQ3oA3dBaocweUEkIj1SB39afrubWOoQzNRBoVNCwJKGaWao2jrhZjkd64S0ZUe4Tl4CTY82G3Lyn3JQqI6k0RD+SZEz8F1m29oYqHumaurqLH1qG/MjYGQHR2cdB22Ix0akc3UBiMKYxiZnsvoCIXA8w5/yCeYOsMyFWEWTdM4iFE0Xmr625bKTWvZfGz5m9U3Jy6tflK8Jd4t0mMNGm0zgljVXr1ReU9vS0iUPG5426i9exe1FxNO2BVXPp+dznqpnCrbVx8pkXR4HbYinwJ1IC7GfVfgs5hjeEKrOKEyR150pZ6i9jjCW5n0+p0LW7G9vklCH6MlvT0bz2PLoOqfSbDukypgrT2TEE2W0eXl551w+Y758gc5/yrMQQtUF9Qr/HP9t5A4xenK6jstAHuBGj8eGU2WIU+6JaKe2zIKPyhnl5EY1IVQI5iGuTKf0OqmUweZn80gZ4JT/0TMQnh59bX97kQE2LWXQvHJl3QMT/LO5PHIGPvWSTbVFrv2s3BRjbfNgpu5ZWTgG0ZZLnhH9rOR8zF5bBiZA0fWzr8swVd+msH2loMdBG+LHvfPZuO4hL+iudUcK0ciuSbf740wd8YqeVWKbwejzlWlgjIG4E3QG1eGt2xGeNuIVcdeprHHXacOCr/Hg52R0muXbRSfL5TG1nOvmpWhdGwtZjch1Gqt7Z42tltfPkWeUrcFiJtRO+UR27nMsZbD9goij51Hrfdw7nz0pj3TmxTWRw0EbYiB+oMaOCGIeGmMN2wFkjwpvUmhMj93fwu64Jg95Harok41oOIKrmaKy63SvgxZsF14bc3tPmHyXxiryJw/Tm98xHzC+74N+VlZ1FT2bc/aMiDcvkr5D0XLnfjJxstp0fiVqIouDN8T1Kj2BMob4k5cGk2ZzRhqhbjpEapMY+xLQ+Wm+Xudwxj7qPKnNamZEHuxic52Urm1uEDQn3xRtnXYBhu0dugk6andDG5aFM+lWf63y4UCoFhMm70Js/DsXRsWoqvUjHuYe/TWGYojTOHhD/IQP/BBec/8XAUj7dC36EcDQ35oLJbKja5l173ymata8GwGF3kB7M8eDqAjDo/VMpdtogIgnSPmHm+sIhKbbM+cvt35wLyLE/s6lj+n06NpvLXW2fHYjIb1hUSriQ9NydNGn6TYs0Xs+7Mu7c6e1vKtl1U32pV4OXrswExD6DX1koi73MrA6BE/4wA+NC0xAMcRpbGSIiejLAPwWgPsB+DCA72Lmzxie+wJ4OYCvQmMGLjDzL7VpzwPwfQA+2bL/JDNft0mZPGiD6T0MuUkWrxN6IUOesejyrOM0jy+s/BdCaoY+NVFkV9xNiZawelIz/zpaQdd37oY6tu4eX0qnzT8X8eHJpvhT7Wfz1eX2vpyiugc/YiIqZ93H6np88q+OqKgc3/DYpGi3V4WKpkgZ+J2A0y+Ogs1HxD8B4M3M/Hwi+on2+scNzwrAs5n57UR0VwA3ENGbmPl9bfovMvMvbFiODrrj6EkRb5IrJR8Z31ANIi3Goh1ETk/GeYYu91JIGbMUPWcMtZHxDIw1yF4EhGfIvF3aUlEHKeSiFrx8prxIvPpYvam20Nepck2Jmki1h02X33WoRiMjBHbSeNFu+OPptujmQZwXzNGinvxVuQ7KiDiNTQ3xFQAe1f5+GYA/gDHEzPxxAB9vf99KRO8HcG8A78OWIB1FDOY6Pl7Nu1wduZ3Xi3boDG9qhMrDyIjAQyOp9aVC4lIvBE2viLPXOjpA0vTMf4o2iMww/Fqn7uxevi5MubSOsfzHyi60Qehion5e/gAG9Rqj6zR9v+2LXHgCE9iMyFO8WudK7RAIxJEYU1+EAHBxeW6gZ1tGuUzW5bGpIf7K1tCCmT9ORF+RYyai+wH4nwH8Z0V+FhE9FcD1aEbOn/Fkc5AHqMlj3LcIxAZSOkDk7x0x5rqDpUZL2hin+AS13ovWjIq8NCCezZcyr8z+yLmoCknTPDlaTkeONzcy9tKm6PTSptYnpwNA14ZStlTUREDseugMY2YhRwiVuOkH6Z5LyOaRgn6+6noRuR4GL4yEvNV13Kdrn2WMGmIi+g9o/LsWPzUnIyL6YgCvBvAjzPzZlvxiAD8LgNt/XwDgHyTkrwZwNQBceumlUdqVH3omXvm118wpTtch9Jlj1chzpz83U6Nl7xM3Z4BDwsim0nQZtMGWzmfLepxIvfjG/JdzdO0aup1DqFBVwW1zQW71XC7Nuj9ybgv7/Expl8Yv3Mst2uiLuTutXfmhZ87iHytTgY9RQ8zM35pKI6K/IKJ7tqPhewL4RILvHBoj/JvM/NtK918onn8F4Hcz5bgA4AIAnD9/Pvkk5oyBPJw5w2h16I4QwlC/Z7xDqBDM8275NI+kaT+slyZlEHo/AiL3us9r/CWTw5i8To9HddPz9XgbYzg973Vg5W3brVbVIB0Y5tnQG1k7yrdpNn+A2j+4PD1fw8vcjNilDFNewKtuMriRFcM898W5CcrKujQ2dU28DsDTADy//fffWwZqzh//dQDvZ+b/x6TdU1wbAJ4A4D0blqdD95m4iv3GTb7TRhQAIsOdk9MjEDta1fBeBGIMGkNvoxzi8vQTb9boGZ+1urZ6vNG7roeXXtf5EWrthGZJOafC1lV0pXTouq1bLym7TqtrDK5tvep6+Ez0vNQZSl0nm2bLBwCrVV+WRSaGmJmwWvVlyPFa6Be9zWtnXyF8/CPiQ4nqAjY3xM8H8Coi+h4A/x3AdwIAEd0LwK8x8+MAPBLAdwN4NxG9o5WTCv08EX0DGtfEhwF8/7oF0Z01hEU36eUt2UyNmqVDj7kTIv9v6F0IudF1aiSuO2AqTRthS9N0zZeKaMhFIWj/uOX16u6Vo2kTf9OfFCyP9zXi+f69OusyWF9/KhRPl9nWX9fRq3vK6Mto3ktrDGHvw7XtI9fLpYx885Nv1rDm3Bcpt8eqpi5qYxcbxXOi7DvGiYvqSmEjQ8zMnwLwLQ79YwAe1/7+QwDuHWDm794kf40nffiZeOm9fw0AMGdJZj+CrFAH6mS9ELV4hLiIPrUqZci0XnkhaJ3a6NkwN2tgBB49ZfhSh3GmjLAtr8ebM85jZbZ52PLbvMdeMrrOubLZvFO83svKy3OM5tVJIja0r1nSvQ19bNlrFT6poxhSRq2PHAIWzibvOUjkiKzQe/pHv3ey7BTw8R8MfQVOQFTXFBz8yjoPY3tKAH1HrhNB5jZETTqn3RvAy0eMb2qzIZ0+lqbLYF8UXjibDSVb1VWnJxXGpvlToWdTNxdK6vCQSM/qMHnlQvMGPslEnTgQFlUiZE7JxRv8NDKC/p6RG3JWrxbR/dSo230eJN3/sor3pVhUw9G0rW8dCHVYdPzAuOthd1tV7iV87UREdU3BqTTEGtqgygY73idlP9KIjWMX4zkwwMPOluITndJZY1dDPi2gp2t+/QJpNgrqw7Dstf5tIbzAvBCvKXxa9ximlmNqmaaUIwpzC37b2XLp61UtYW49LQRCAHV0YFoat/c/dJN2edfCqo754lh1n1+eIcl/Sljb1sBr53MJEV2vri+0E/cATk5U16Y4VYb46R/93s49oVEHivYOtiNdi66jqfAxgfUz950sv/lQSJwGkUrrfcuxL5M5Nsy6vLb8MS1f111gju59lMP376ZlNb++TyHQwPWwblib6OyjM3z/cOe6aPlCxi+s+a1LpKqC+1W3dbcEMOhLE3EzM59P6j0hUV2b4lQZYo26rtrPzp5Gra20k3hA/JDUoerkRCblMxZezac7rF2OqtN1p7BpEV3lp18mnX/b1Mern63jFKT07EpuH3naNhlrS29T/8CEUIsLIET3RdwastrNS0umA6iXC1Dl71us+boNhzK8mh9ojLjsQSFyO4uawF4m605sVJfFqTPEy+UiOYr1oEcW3qSLxz8lpE0bc6A31EDfCW16595Y9aNgoad45SWgrwEgVDR4mehrL41ir0pXVpvm8Vp9m3zu6vp6+aTKMthQPfj1G2uXGhTlUdc0yLMGuTqW9SLaulLoq1C5L3Wb7r5AQ6MXGA9VC3X/Upga1sZM4BqR3C6wB0N8YqK6xnDqDPH3feIZuOaSl42OjqSze1ENGtrQcRiOiLrRbcsnRnoQCWHSBVEIlYrF1XoHUQSK5l1LuTu5YA/N7PUHFQ8bTDwtGYMRuUOcPAf5KkwJp/J4c3XT17bMU+rHtYnsCMN70UVpmOvuU78e0oQ3MshtfkAzENYvw24wsOpDyLx0eYHXoR29mpeARRNxgUinblcPgQnP/OTT/cSNMNxDZdc4SVFdYzh1hljgRj3o4HrnBA1PBmg6lp0cc485WvmREKl07V5YLYehc95LYhA9gXgkKH3Nbnoz+beWNXq8jXFS0Ru2DLnRkK2jpy8wuXX1ypf7reuc4tFlshEUXrSEltOy9dLhbXWQMpC2LXLpQOO3XtUVUPfREw2dIj3C20w80oA/JbMLNPMaO1N/8DiVhviZNz8N11zysogmoWcy6KnaZ87rCNKpVnU8WTclUsJGQgAYbtTuRErk6EA/mx5COnoCGEYLrNRkn0RgWL4UXUcO2BeRXKeiC1JlyiHFpzfuSUUxpDYpmlLnVajakyz8MnjREhKtoHV40RA6skLTvQgKLx3IR1Cs1EhdjL5nUOX5u7iSFxdwtBgOi59589PcvLaBsh9xGqfSEAv02XF9eFLqoe6NySr0BltQkWMwAw0Mu9an8/XSdZrW3YyEY3oXrqRoITKOHMnpMlh+/Tmeont1SV2naKJTyrBO+jplEb0SYWLr69F1W0obxgs94mu5Hwt1FFEdqJPTdZL7vHAMai5NyhLqRq+8LLTu7jltv5Qk/7HQQYkxlnznnHe3LvbgIz4YnFpD/Mybn4Zf+pJ/010Pg+xjo1aHoW8XGBpuMXZ2pKz1zUnX+qUMNk+PrmmVGbEG1Vl7P6oKg9MdIvV7WxjTuavOOaGOuk1s+wZG63JAd7+a2FulqvXXalrgRq81rDJ6lfvV5aHSACTT6/bFrdOtfgCoGQjcbzKvXxR2cAEAS/maq4EfvuUpQ4YtgXFM8coHilNriIF+lBvThh2uNkeLe6FQ0hlzy6CFJxWvHHi4ibsrB3QvhyadOj9r09F6/ZomnU+/UOzvdeC3Rz5cbJ1wsnV0bhIqB/RLkPVv3YYcvfwq1Ijvex2qjkfLBbV3gy4ruDkp2Yar6XRO+Ia7Jcg89PVGPOhX68nEnn2BWOzcf1t8xFmcakOcgjyId0w4AbrrkAlfrjZ2+kGzBl1W9dl0OHKWP/I3Z2gBvT6bLv5Gu+xav6xyS8O9Jdma5smuMwLSMp5uXSdL00jVy6t/UJPmzYu2z0P8w1qvpfVuLz3Z20+SeT5gvfTc1kvLeem9fLxCzkMIhNAuc/Z8wseJ4ppI41Qb4h/57FPwwrv9m8goAfJwNr/tXsDWiMmuaKk9b8UPrfe1tZNpzH26XQ2n978VuZqHk3tSRo+mdfYPu/W9Wj/p0C9tf8fysZylDfNP+5w9WF5bp/hl1Rqn4PFTlO79tj5i+W3TBFo2xSO0yAsio9M63tJS06uqn7Cz91nSc5N1srXm0ZHWH+tp6gBcDM3eylYfc9NXCvaHU22IBcz9UtBmQkM+JZHYkFzifTHg0wYrx9PriUeK/eRQP1LTcpoeb/auwt6q/kXiXw83MI9PItEvIi3by0hZ5LrfwHwKLS67ILdZvm4Xr05TyuHVVeiaR9fXytg80tdNmRdq4WS/L3JfN+Ft9hkePmsis1gM6U25ZL/k3pjbtmIGlsuYx9Mlz+Ny1fuw5+xjvBmOP474kHDqDfGPfPYp+Gd3vhZA+rNbIIY1tcBDDGZt4oorNQIWPtGj07rOFWgg58lofs1b17HbI4SeR+tq6hSHlol8X+f+dxcKpmQsv5XTfDbfMeR4tPHQ5fJ0SLrOPwpzq31+yUePtnW+zXOg9hNhAnM8el2u+lhhHSkjfJbe+GyHo9flqr9//Vdany+4kW+2thy+1Hr9Qz0emjI20R8/fvuT0oxbAqP4iHM49YYYaHyAFTzfbm9o6jZus4IaLRs+5qbD1NzwaR9l1Y2S4528IsPK8QhaoNM836POI0cLbbmiRQ8q3cpL3l2ePJTBWOdh8++2wInfI2XoF2eoZNOuli/e8jK+FsPWxYw7Mrp9BTKpO9zTBG30xND/24WyKYtlfeYX6wqLwJ2xjYw1eh9zAKGquZ3U8/mA2D++axQfcRpnwhCnEJi6kQHQGk5ljIUH6EcQ1oBqY6wXA1jDryeFJE0WFXh00aUNvn5ZaJpG1wnbOiy56n6nFkzYkYrVMQVztrzclU69SMPCq6NAt5GnQ0/O6ftmaTUTFspnXnP80o4nVf1JxIA+9n0R+aDj8kj5PR55nmqmzq1xbgenbswCxy+VghhnwhA/5/ar8HN3fkVkHJfd3sS+y0I/1JqvM65qlN10nl6PTV+pkDedVjsj5IB4g59A/eiuK5OhETUGWxsIHSURVAVt58+5a3KjJW+UmRrtW5rVk5QZy3NGmXP1tm0khjdqU6OrBg1oALBSspZX9Gk6B+BIVsSZyJZlaHzY+iWh05vJwTg/t00CcJGbKA39ogggPOf2q5Jy28YeTug4GGS8SKcL8sDVTLhjVWGl/LxsDR2aEeHFumo31I7TBQGEVeiD8S1fzU26l7YKFPkntUFnNp2NY5+gXEeTMBzLap+p1ZFKS8l5NFsOr8y6vay+XLqtt22nqeVM1RHI11+u9YvSlsPqGJQZ8XMTArLPQs2JtLp5hjwjLP+u6uZZlefW46vb5/SOVdXldaxGGAAzzf47K9hoRDzllNSW78MAbgVQA1jJRs9T5beFO/SeEAke5tYNofiGG/z0PKLH+pNXdZyu05Y1ufSVjmzo4o9jd4eExGlZyV/T4g2M4vJpfu0XFj7Ja7gwZZiX9YF7A7MpO+F56EaCxvdt78Vw4x+//bROSbN+3chvy3FeQltUw/rafR5C3fNpel03I2BLl1G3K9OGqRGGS6E7efTPVWq5tPCtAmF1jL7hBiVqIodNR8RySuplAN7cXqfwzcz8DWa3/TnyG+O5yyv7ZaMtLZrkCISLtaxU8/nqQO0IJk6H4tPpXlrgod5Va9jZ8OZoganzTWseua5Nur2W8mtdcl1znI/4JYOS1Xq1Tq0XSnfqz/LLdW3KLnSvPF6ZdZrWOaWNLL/W1dzDuNwpmr7HTbliXo06k6bTa0cvWt01o3mG1dyHx/fc5ZVuHjsDp7+Kcn9nBZv6iK/AyCmpO5bfCppOHdO8rrAcjJTb/SPaa61Hp3uyQN8xo0k+6ukadv+BHJ/m0fXS1/KbASyMLq3D6vZeMCn+bSBXliltBDRlJgzbJpi2ydU/pXuMrx+t9zzdM8I04Je0RetX1sbTyi6ch1T4Vwyg5TkJ41B5ORb42HREHJ2SCiB1SioDeCMR3UBEV68hvzU8bxWPilcMXGxHqbZT6Yd/xbKhSi8rPIGBiwFYBj+9dmQ1XQyi5WejZxmGfHYkqXl0OptrnafNz+qdQrd52XJ5aWPpXjvkyuLVQ3+5pOoo18K31P54lR4Mj77W+QWTp6aPPReStgzNMxUS6fqZFJpG/1w2e1No2eetjnk03KKMiNMYHRFv6ZTURzLzx9rjrN9ERB9g5rfMkEdrwK8GgEsvvXSOqIuam1EqIKNLAkHtkoZmJNEYtv5NHn+AS4eO07WOwL3ulJyW0fxaT809Tcrc7QUM7q41j4bVDfS/x0a0Xvk9+i5Gxb0RmlYGi3XrveImFC2o+yTXlkfKyaB29B3rtnQNuWeDHdfaPFetnB39ik4AIObuq8bTsQyEJYBzlX5Sjx9nafJtLkYN8TZOSW2PJgEzf4KIXgPgYQDeAmCSfCt7AcAFADh//vxGXf55qyvxnOq3mjokeMRlwNATbs3DHbjv0PbzVhvR2OXQ07Wclmn8keM0BmGptl+UTtnw9TzaNdKnI0qLZdNtJl3Y8nldO7VH8xjG5OaUQfj0g1IrV4D1aXd7EKu2s0/HMvSGVxs5z7g2zw1HZZbXuH4hCL1mDHT3z9ZQTqezkk+3XTM6/rnwRJ/hGLAr99VpwKauCTklFUifknoXIrqr/AbwbehPQx2V3xX0A6kf6MbIES5ys11hQx/yXWTCkoef2EDTES+aiaJ+xNTLaZ2r1r/MilYrmhjL2vCFjt7TtK9ap+trGHnLq3/XI3w6XXe2wPP+PDkvD1sGW94pdQSGvDXL5Fzc5vLH3X2iqM1ro19oK7YGE1iyGGqK6Aw0z5wZNbKSW7VyVifQhCNdZGrnIoZ8APZqhNdxSxTXxHRMOSX1KwG8pt1a8AjAtcz8+zn54wSjn1gD2kmOFlHQP2RCDghAF3IW0LzNOiOYSAOazmTTtE4ofkuTMuiY+O58OiNraVbG6s1dR/VL0KeUbQwev6430LRtqvzr1AvOtR6ZpORTfFGMsfxgdJNuQlu1tAWG9yuXJs+EPDfWTjEDqzbPI/PsngSUybo0NjLEE09J/RCAh8yRPy78XHginlP9VtLwAf2DDzSdJDJ6xqiuVEexaUv25dDm3WwG3/PrTtdt+2BeEsHQLJ/lIRX14KWlrvVeOzm6ldVIjW76FYVDXm9PIF3HOkOnCdddeFeGV8oxRgtoDN6CYsO8bGmDurV5naOhIdfRLF6avJAWTtvJ9UWOefY5GhacpRHuXJyJJc45/Fx4Ip5Nv9VdE4bGtLZGB7GBlg6nRzAiDwAXE7ql83b0ln+p8hK65oVDE521wwPvms3IcDC8go8EXcoecrJz9EHpW6dc5npOWzA3I1LJnwEsdP1aGhm5gOaryJZdRrmWzmgMpt4BU/KrlS77spL0upWN97EY8ryA92+EgWKIczjzhhjojZd8amuDaY2wdaqn0rvRrkkT3fKpavPUdLT02uG1NJ2PxzPleko/SfFN2PVyFnL65pZVv7CmXHv5LzFsp9qRsy9Mj1fo2oCb7Yij52fBw+cukrUvElOWgpOPYogBvJCfiB+l3xoYuyX6z0Bg2Ln0KFjStRGtjZz2ZQ5Gzwl6naGRo1N74TTPnOvTAFuXeua1/tIIhke39xgNGV6d1wrxKFzoaNO0Ppj0i62s51d+4UkZDaP4iHMohriFNUqeseNEeuXoECOsDbvIDSbm2uvI3ZHQY2lap548s/rkWsvZdMk3JTsWYuPxeH73Kenr5GevU20xNhcg0C9BLad1ezRPVvN6I+aAeGRs8xUZm965P9o89cj6pI2Gj7s8h7QXzpnZfW0ML+Qn4hf5iY3fDoy6fWzsAy+jF52uO2Ot5NnI1RiOcOXfpeLXnct2VCuvabVDS8l56Vpep9nr2uGP/KSG5tHH0i3Ny3NKGXWafgna9kq1n6Z5ckJbmTIzmnuq66Ll7f1o6Nw+O3G79O4NHjwTMOkX2+foF/mJJ2Y0DABgzA5n3ELc8cHshVMMscEdUeeJjXGNpnOtWopODypdQ3e8pmM2f1pnrfRp+pC3MfBCkz/h6/Obdy06bDo711pWy0ua98eGz/5NkROE6CUX8+lr3Z7syE699tpaygfE7a/jzqWNaocewFi2dG/kLGmxq6r5tVKycNIDmmf4pEFim+f+bYgr0Oxhg/bf7zhm+ckohtjgxeyvw69NB7DGetmme2mrRFozwmEzCuROVz/aEwOMiBYUn8hqfSuI4c9fi4xO9/RZWfunEY92OfvnyUi72b9UWbQh8upkZXPXVt8yKq8eNce0/r7psjT6akOXZ8a+1HXaMpEmL3V5GViknuF9Yw8j4oPZC6f4iB28mK/ED9Ar20kV7ka7gB9ZoY1hlUizcjA6LT0XTaFpUGlAPLpKRUrYdO9aJpa0PhtJMJa+TiSFLQ87vz3d8Sf8MLohdS1yVv/ws38Y8cAY+nptufTXkue/bUbEjIXJs/89TJPyBQALcOT7PqlGGFjbR3wJEV2vri+02x0AwInZC2dTFEOcgO5ANRiVLBnFMLJCGwtJB4axxUA8webJ2BG3jYgIhgb0s+V2RAkjNwcSnbEJvInCdXjm5LeODhsxofVJOjAeRQH0/mIvAiJF1y/LVJq9x3LvL4KxAEUREycRjLVHuDcbv22s94TshbMpimsigWv4ymjSTiCdofnkHPpbgdiAe2merzhF1z5kj9Zfx37Xvqyxj1P7VIHePzv02+b9ulP+9ATbJjxz8hvSYn+ubQPdRnD06ZeR9fd6NM9llKMD2u/sjXzFheV/9UgZLoJxzQkeDQPr3dMN8TocyF44ZUScwa/zVfgH9Ip2RNmPiqUzx6OYJl06nbgXyKRpA6rTxuh2gkzTKlBnTPw4Yk5e6/z0tQer30vPyU/FFD25slh5RuOnjeOG89eiX9pWaB5fjWZUatt1NUIH4nuxROOGqFSayCzRuCAWKnct+xI+vvPn1sUedl87mL1wiiEewUuMMQbQTbAQKHJV6MgCnaYNnXY5SGeXURuBjNuDI14oeaFjBp91j9hrgdWRi83NyW+KXDk2SU9dWx29WyrPJxN+dkEFd8+Jd184elEIfdXq8WTkOTlSxvhQjDBw/K6TQ9oLp7gmJuAlfFX3qXQx+rSU0WoDHR2h0+Qzt4kVbv7TfsZayWhddcurIyZEXnRrWo6vzsjpKACrAyZNpwu89FRI2tifp0vnk0ofqwebcrMjb3Wk5Dw+iYAB4rbTrgtdFonAYaMbsNEUcVoAuljhQzPCYY2/s4JiiCfiJXxVF2NsOyYgnSedZsPXpDPqMDWhawPh0VPG15MfkxuT0QYcJr33L8ftYvnndDZPl83LK4v3sklde4Y0JaPbLCeXk5f7bPn1c2FlVkAyLaCJFT4UIyzYg4/4YFBcEzPwm/wkPJmuBRB/3i7B4PZ0BWJKpjXHwjeyXeSDHEmv6EulS+je6CA1YtCGIRVJkOKxvF7+mt9LnzuSmcKf0m/dBlPbydLG2kjysuWI9pOgZpR7Tj0D3j22/IGbjhi9UDNpQPMsHhrO0gh3LoohnglrjGuKY4iZuDPGXppsfCJpQq/RGF5L1/yiR/SLgRcEpkZG8Wl9OR4BydHrJJ/WQ4gOL51U2rqgjP5g9Hv5A3H5SdWZjX5L83h03qK7Myomv4DmRWrla+JOr+XHjDTgMI0wA2UbzAyKIV4Dv8lPwpOq3+w+RftREfUGVKVFk3oUh8SJDCCdlQe6hL/T0xmMZoIPrW6RFz7YayXb5NHwROjOTet1D6D0emkbI6GDwdGpKZPTSbd33AZAO7lKw+s4PtinrUhHQIirBB2vpcPwix7ZQN7KAIjSrg1PTjfACUcZEadRfMRr4trwZNfvq32Ikub5+FIy1k/rTZ7FsbE84M35PbWvNUVjo9v7z8t72/95ZZ1aJi3vlVW3sW3zMNJO3n3z2tzet9z9R0YGaFxch2yEgeIjzqEY4g3w6vCUgR+xGd3Y/SP6Tm535ApGRusChjt46eB+HeRvO3gYkQ0JHtFr+cJIui3DNv6szrnlsu2g+eyEqNcO+no1IgtHRiboUnR28vQm9QKaZ63g9GIjQ0xEX0ZEbyKiP2v//VKH53Iieof6+ywR/Uib9jwi+qhKe9wm5dkHrDFego2LocEKje+Qnc7ZbG3Yj9ikw+tZ81xomhgHq9vq9GhWvzfrb8PFbLoeaXrhaGN/uXA221YMdss0pS65qIlUu62Mbk8WiI291jncHrP57w6K07qy0/ClfBqMcAlfy2PTEfHofp3M/MF2n89vAPCNAG4D8BrF8ouSzszXbVievUCM8ZK4m/gKFBusFTVLOgL1aQzuZIQO9B1SaCl6ZzAMr6VZWYGl6fLVbbk8PV66huUf+8+T0zSbV9euibJKvlre5hO9LCmm6bas9cuT0rI5nUtHB4OxohC/YNqyi8xpMcKCYojT2HSy7goAj2p/vwzAHwD48Qz/twD4r8z85xvme+Lw6vAU/J3Fy9vVbe0EGQFiiuShqqK03pfcTdK0s0aarvljg9LzirzwRpOIRJFc7UxoWZpMxKXkbPoUnVPhyen81kmfQgsAZNYu+qKhYVsENbun768rj3apNA35VxSiSc+uPcF4bfjuYaEPGGfJ5zsXm46I5+7XeSWAVxjas4joXUT0Es+1cUj4nfqpg89YWXGl95TtRsMIEV3kLF3zr1QnFv3Cp90Eep9c6fjDvY/H3QWevzSVnhrNTB31zCmP5Z9aHvmt27Hn8d0h1sWxau+FdlWglRc6ojwZd5DPr1dQarnfqZ+K04Timshj1BAT0X8govc4f1fMyYiI7gTg/wDwbxX5xQD+JwDfAODjAF6Qkb+aiK4nous/+clPzsn6WKGN8WrQMVWsadSR407u0a0eMSQ1AurWcAdF4+5aOnvoZDUtRPmw0RX/WZmxv34z9+3wyV+NkOWXMur6Whm07aRp3MnGckLTL9T+hYkun94vzC7d8ms9eknzaTPCDaY4qdJuq9OOUdfENvb7bPFYAG9n5r9QurvfRPSvAPxuphwXAFwAgPPnz5/oO/Q79VPxtxcvi0arnUuiM7INxJWhR66WLjrQ6gGaT/FlSxVeIN4VLvqUVrIaWs7S4nyHMr1eJK/XwRQdw0//YXpt6ms7ttcmQf1f0wICFqBBvqQMak9v7jE5/ECI3Ch61A0Av1c/za/wKcBZGuHOxaZ9Zs5+nVfBuCVa4y14Avp9QA8ev1c/rZ08il0MNdqJGPlTaasEfUkh0iOf4sK7UvwrQ4Oi6U++VctjZVeGT+DJrUyZPJfBup+jlmbzzbk1UvWydQumDrbtNC2Q4xqh5t5oV4a+Jx59SbH7Q/TUxKfaCAMljjiHTSfrpuz3CSL6IgCPBvD9Rv7niegb0LT5h530g8abVs8AAHzb0W+gUsuegf4hC8SomLr9JeDQu72JKd46UUAtv0cLLa9cQ8mLLi2r+aw+LSOo6Xi6i5ev3irS8rDi0TTbBgA6Y6xpXlt7tAAGE7BgGtyDJbFLr4khS6MDMd7YPienGeIjLvCxkSGest9ne30bgC93+E7XtHACb1w9A48++o3O7ygbf3ebxRNHaZYOoEvrjAdCxCs8Ar25eI4mdEuz6ezIeXwedi03VnavvlYuRxN6AONIfUTqe6OrEN1nQ+/Sqbl3bzoDRliw1h4kZ2RYXPaaOCZIh3vU0a8POrv4egl9Z61AWLVXhNig2IkoMRg1hgeQj9FEr85XYPOzsEZuzHCmMEXOM6gpA+ul9ZNj69OA/gWo0+QldYQqakuJ5Dhn6Azgzat/MFblU4cyIk6jGOI9oHMVoA81syNbSdNHLEn372OMKTr9Q3QjQdM6NE0j3pCIuzy0jMcLDE9C3gRWl81LeKRcur4anpzHK21j87R5aAzLFwb0gCYqItWGZwXFNZFHMcTHjD9YfQ+AZmQMNCMsDe0GsCNnVvx6hCijZ/nt8cq/S4dmR9Ya645y9wFvFCztYUf3Mnq1NLn2+HSa5tX/WheH5Zf7fxZxlsLR5qIY4j1BOuQjz/0rEGPQ2WtqjEjgmC7b0gq9AmFJrWuDYyPChEi3yHs0rVNkRWcOY3yS35iOnPy65eB2hWEwG+EyASsOk2hLhGbCTbUpkOe1bhQm4A+WZ9cAC8qIOI2y+9qe8UfL72v2KBBjCO4MqxhJpt44d4sPWrpHszJTaFZeGx6dl/zpvC2f/sulTeGRdtB56jJYvrF6SZnHaFpe56VputzSDksKUZvUxPij5fft8hE6CDAkuros6PBQRsQnAH+8vBoA8IhzF6KRavRJnRgSplwH+lPZ+3wem4izrhENO8GXK0sqIiJVXk82lc+Y28S2paZPpVn56MuFfP4lNRN6cl8LGpQRcRrFEJ8g/PHyapw/dw0A2cRGuyV6NKdE9DPzBIpo8UKH4XHwHs2TtyvmrM6eNl7OKUjJ6nxsZ/aOtffLPWwbj+bJeyvyrA7L+5+Xpyokfivgae9kI7T1YpxIFEN8wnD98pkAgPPnrunm2tmM1jRd/pWz2/SyWkFoaTkdnrx3Xp3waKOT4kvRp8A9K8+UOcfntYGWbSInKNtecXn6o6E8HUKX+1dQMAfFEJ9QaIMssau9a6GBjZQA0G14E7sE4gk/oWm+vLyF/8mvMWWxxRz+8TLFfMPohTCQD63nckzei6oQrBBwhKoY4BF44X8FPYohPuGQDv7gc78ShWJpn6b+PTTa+VVznr/V47fhWTneHH2sHFN5xsqTq4OFbSMxrjY/vZhD2vxdyx8cLXdBg+P2ERPRlwH4LQD3Q7OFwncx82cMz+Utj+BrATyXmV9IRM8D8H0AZLvHn9zV4RUlauJA8K7lD3bGIEA2CGo3pWkNxgohoslfv+FNng60BkZFB9hJOSufu97kb0y3Vx5ddp02tf5Ru6KPXLFtLfegGOE52Ms2mAdzglAZER8QdMd/4J3+BYD+5AdroOSkiIhOw9GjPjkixav16hGhlu34cufdz4Cna0V+9MIg+oGGIwxPX/cCs3UABjqE/o5ifNdC45o4dlwB4FHt75fhBJ8gVAzxgeJ9F58FoDfIwPDT24aZxZN16dCuFK8Ne0t9/uf2fEhhzh4XKd1jbhKvTjr/VJ2Avr0L1sdcd9UWEJ0gRETrniD0VADXA3i2dW1sC8UQHzi0gfi6O/2S60cWeMZmakxxLl3rsnybdL45fl4v36kvGI83gHHjxR9eu+wFQ6wVvgZcQkTXq+sL7SERAJoThAB8lSP3U3MyUScIPUeRXwzgZ9EM6H8WzQlCO9mtqRjiUwQxHGKQgbRxssY1NaknRmlqxMK6WFdWypYrv8bYyLcY4N1gg6iJm5n5fFLvCTlBaFMUQ3wKIYbka+/0wsiIehEXlm4xdTS8T8wZ0fuhbQ39Qxd/ZPeFPcPYw5JlOUHo+VjzBCFxbWDHJwgVQ3yKoQ3L/e70i0mfaj9B1ZwKMkg39JMEKdugboky2xfOhy/+6M7LWNBgD5N1B3OCUDHEZwTa4NznC5rDsrsRcXucD4DBnhZ9dER6Yizntpji1hjjGctrRYnFGmTq1uq56Y5nZ8tTsH1w4otrp3ke0AlCxRCfQVhDdJ8veEG3gfrCGDQ5ZUIg6Zp/bnTDOjy5DexT/DUYC6JieE8ITq5za//YyBAT0XcCeB6ABwB4GDNfn+B7DIBfArBA80nw/JY+uvKlYPfwDNU9v+AXXONsDbOmW1ijPoacDkmrwcmXxQKEj9/xj2flWXB8SO0gWLD5iPg9AP4ugF9NMRDRAsCvoPHB3ATgbUT0OmZ+H/qVL88nop9or3MB1wXHBG3QLvmCn58kY10IY6PWOTpyIXE33/F/zs6n4HixQdTEmcCmpzi/HwAov5rqYQBuZOYPtbyvRLPi5X2Yv/KlYA/IGbovvfPzXfomO695Oj5z+2B1asGBoZjhNI7DR3xvAB9R1zcBeHj7e+7Kl4IThpSBTBnodfUVHD7KiDiNUUOcW7nCzLm4vE6FQ5t9R4joagBy5MEdRLSzmL4TgEsA3LzvQuwQyfoRPccjHxJO+727fB2h4prIY9QQ51auTMRNAO6rru8D4GPt78krX9pljRcAgIiuz622OXSU+h0uTnPdgKZ++y7DacRxbIP5NgCXEdHXtOu5r0Sz4gXoV74A4ytfCgoKDhhhjb+zgo0MMRE9gYhuAvAIAL9HRG9o6fciousAgJlXAJ4F4A0A3g/gVcz83lbF8wE8moj+DE1UxXqOxYKCghOOvexHfDDYNGriNYg3URa6XblyHYDBpsqplS8TcGGc5aBR6ne4OM11A9asX/ER50HMpXEKCgp2i7tUX80POJo/EXvD8gduOM0+d0FZ4lxQULBz7OmEjoPBQZxZR0TfSUTvJaJARMm3IxE9hog+SEQ3tiv1DgJE9GVE9CYi+rP23y9N8H2YiN5NRO846bPXY/eCGryoTX8XET10H+VcFxPq9ygiuqW9V+8goufuo5zrgIheQkSfSIWIrnvv7JmBU/7OCg7CEKNfSv2WFINaSv1YAA8EcBURPfB4ircxRg85VPjm9iDDE/u5NvFePBbAZe3f1WhOQzgIzHjW/qM6ePJnjrWQm+GlAB6TSV/r3pXJujQOwhAz8/uZ+YMjbN1Sama+CECWUh8CrkCzxBvtv9+xv6JsBVPuxRUAXs4N/gTA3dtY8kPAIT9ro2DmtwD4dIZl9r1jZ7RbRsQ9DsIQT4S3lPreeyrLXERLvQGklnozgDcS0Q3tSsOTiin34pDv19SyP4KI3klEryeiBx1P0Y4Fa927YojTODGTdSdlKfWusKVDDh/JzB9r9+R4ExF9oB29nDRMuRcn+n6NYErZ3w7gq5n5c0T0OACvRfMpfxqw1r07S4Z1Lk6MId7xUuq9YxuHHLbx2WDmTxDRa9B8Ip9EQzzlXpzo+zWC0bIz82fV7+uI6F8S0SXMfBr2oZh970occR6nyTWRW0p90jG61JuI7kJEd5XfAL4NOzzMcENMuRevA/DUdgb+bwC4RR3UeNIxWj8i+ipq94clooeh6WufOvaS7gZr3btA8//OCk7MiDgHInoCgF8GcA80S6nfwczfTuoQQGZeEZEspV4AeIlaSn3SMeWQw68E8Jq2bx8BuJaZf39P5c0idS+I6Jlt+jVoVlo+DsCNAG4D8Ix9lXcuJtbv7wP4ASJaAfg8gCv5QFZPEdEr0OwTfkm7hcFPAzgHrH/vyog4j7KyrqCgYOf4guq+fK9z888O/PDFHy0r6woKCgq2Acb4ga9nGcUQFxQUHAuKayKNYogLCgqOBcUQp3GaoiYKCgoKDhLFEBcUFOwcDEZNYfbfJth0s7Cpm3FtA8UQFxQU7BwyWTf3b0NsulnYnM24NkIxxAUFBceC4zbEW9gs7Ng24yqTdQUFBTsHA6jpRE7WeRsYPbz9HW3G1e7xshMUQ1xQULBzBP7oG269/TmXrCF6Z3MIwgVm7s7NOy2bhRVDXFBQsHMwc26j+U307nKzsEmbcW0DxUdcUFBwlpHbwGl0M65toRjigoKCUwkiekK7adEj0GwW9oaWfi8iug5oNnACIBs4vR/Aq9RmYc8H8Ggi+jMAj26vd1PWsulPQUFBwX5RRsQFBQUFe0YxxAUFBQV7RjHEBQUFBXtGMcQFBQUFe0YxxAUFBQV7RjHEBQUFBXtGMcQFBQUFe0YxxAUFBQV7xv8PS7uN7LQTGTwAAAAASUVORK5CYII=\n", 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n6LqMvCTT7onGqXOniNzZYhMNRhKW61EuIuoI7U13w+LSBd+/glFHxAXs6sy6g9gl/zTR3nS3PsBdRrPW1SDpDdKlxCIr5esANBynC3LpU7I58pPENnUrtYWIlqrNdToVZC3ieyXlCMGHUXT7h3cHGr7YhFx9xFlsTcSHtEv+aaH9w7sDajRLbZjIOOKxU2MNmbdUuQOSYeiwioxSt8EcWU7u6e0CST1Sl0mk58mnZI7bp3+R0UisSxo3CrKyVUFm84HQ/uHdsbjvRdwLuYavlbCLEfHB7JJ/0uh+7x7gKzhd5dbRsPDC26iHWpqQORv8dJl0QVhdlu7RMObzNwWasLtLKFdAvDEPxSPcdtRJ5FZm7OfsCnn2fuE+Pdr5ba4MAInNcN/bP7w7aEloHvC2zdrlPELat8LFLoj4YHbJP3EwgY4JWIQeqR9MJqAl0MIM99owpLOyTo0wtEzbbBsQ8UhKIgv53DySLycLaQSkds21bgSP2YXootFr6nogRt9exIn7YZDpMrRdYWpDnrr9EdpyXF2nZKG9Epln83hHwdbnDSf50j7n2AURez0u1+Qb75JPRNcDuB4Arr766m3rfKrofu8efUcfPn2b3rcLxITV7VA2cDal+jZPTtYA8VBPyT2bu4BnU0LG9Mo2ASnCHOTO9YiNBtELDMSjTI6KEvLUeaRuQyyxkckLwMr0kFvbBNC95p4A8YUZGdc44jx28Wre2S75AF6E3tWRgJkvM/MlZr50xzvecetKnxa619wz7sAcRl+rZuykQhQ5mc5byic/XllDhUweCcnqnLJE1+qIXIdzWRu7+rF1tmXp+nny3PUKpD11XtuerWnTOfenlM+U1b3mnrgQsO065+eCYBcj4oPZJX+X6H7nS/pfjsKoKxnBqpGS7piDDuU3rdlkEi6XBxN5ShN3ojPnkzM3kTc1FEh2gDMj0cT+RJ1llNoaWTRSnplHl+XtVCf5unCPV9S7c3RZQSbPS/O1b8FBovqIi9iaiA9pl/xdYSBhpj7sadHFkQ3yQC6bUWZJWDrwEcdEK4Rgfck6KsDz3dpwtlyeObJ1dKYwFWmRDbsr6Gwiy7WbzaN9vyJr0JOs+JkHO0HWkvFdsyJwimx2v/MlB0rGF2uEuy52Ekd8KLvk7wIRCQOp/1bAiowb9MRqJtNGXyOPJCxYGptaJr5PLZNP7mTjXqm4U7c5Mk9nl7DXJcgdXuddu8BehxAlKQIuXXurRsBD/j6EbZBp11DD/X2ScvSEoOdrDvKDJeM6Is6irqzbIbr/+qVhHwPjapDP0oUzohs+eZtUBvR+RS8fEBODyIZRmePuCMumXZcHZ8phAkDpiyQhZ+Vq2XbmQft7xaZAEyEwEtkQCWFkXrpupw6IXAxRGyK9l9aVIT5qUiNnaful9gcbe7buhqy7//qlaL7uzcVmOk9gxokdcXgIqES8I3Qvu1dPZkfG1cCkPuEpdimwSpcYVj3SK+UbRs+c+qC1S8SWI+t2S9EJHpbNPPeD9oluAnstYnOoo0rX8cLav+vlHXZh49EV0Ki0Dn26tteq+gz7WXBMujlbNo+0C0w+yavJuCVg1aB72b3QPORNOBhU10QWlYh3gJ6Ewx+rpue6RWcIU8mjkaeSi7tBXBECNvl0npYAUoQUyZr1bAFhxMxpuujmYocjjqbRjr1OC6tjO6vNpsuX69d6gxsHaTowRpCQqpOssNMTSjqOWNvSE6K6bGurlRcrxnazo3hrk4IrQ8pjHBYZV9dEFpWIt0REwnphQHTCpZF3PHZSLQPiTmplnXIR2Flohm/PG8TmOsScPEIwpfy2/iXo6/X2h7A21Iq1QUeXpeXRnhCqDa1dm0f0bBtrYm2dOliZfXGJPT2pxyofSL0oRvlBkXGFi0rEW6B76b3jvQyGEU/Tf9Zeob6TS3I7Sbcif/IOyH96y8SRXa03RGaYCb/SpJaVleSlz01vcs1i3fylybRSPbVMrZLribNRLgdFyLq97Go9bxKw4djlIHmEcJeLUU9P9glBD26OVN699N5oHvbGXEvtPxjg6prIohLxhuhe/GUAFj3JRbHA6mE7PsrLmfJycWksjC+3ozCp5oWu0RjWpl0iQPi89twEzug2J0tGq7mGyXW2NTuhrZfXvrYe7kjZySdEp0fC0oZCyMtFqi+QUDRdhuSJRrs0+qTtkmdNtkzxyryMvHvxl6H5htdPt91eggrPRkUl4g3QkzD6TVy6BuT6dfvfedn0JyGbEe5wBL3kt/laAvFizKeIILGpSWK1SEaB3AGkQ9fmjBqB9UabFttM1gF+p038xxy/2DTmhONJ2yQhbE2qH0a7g74QuZDuahHL1KIPViN7aigmY5vPknGQ93lxvsm4joizuKC7j2yOiISBvjOvGvDxYnzQhEzDaIZbGuWahFV+kWnb3BJ42UTpg0xm+bWspaHMoVxtq6XEVpQe6qZlg53WyJQ80snpzv0xduy1eLpJvW0bqLxDm7cjQSbXottS7mcgYXsfwP090raGPPreYsw7yI4XUf31s2Pl2rY8g+cNzLT2zxSI6FoiejsR3UxET3LkDySiDxHRG8LPj8zNe5qoI+I10P7ml4MWSDtNJx2ldydQozqcIx+g5K4sfC6zXkmn3A4soWvaVtuk+sMFNH0eDSnDs9WZsq09keuwOw9THaqQl72JNJOHl00sJx7bX0czAH19Q90jl4PkMX5a7lSbmBC2SF/KkHSktqTOab6xLGrCNTtysU1N/ywu/sHrsu22d2CkX1hbYs5e6AG/y8wP2zDvqaAS8TpYLsBdF/tu9adxcBOw3a7SyJNtMIdJukVe5oWiSWf18oiLwk46eZN6Yku7TwRsSMFimFRCmnc2Mvl03e1kmc6TyGmcOLOuDm1TuxbU10Xix7XbZ0q4mTeZJ021XMRLnQEV9taM9k1Z3JblIO5fPKV7sq/YvWti3b3Qd5V35ziHd/Ns0L7gvhhicFeLvnNYkgX6/5cFeavkIpN8klc6mZbpCR/lxojK02UNZBN/GqNtxokoVjpix0sXG/pH6iNyPbllddf9EUhb6Ov36mHrKHlz6ZrEdBvpdsu1rX4xStn6Xum28O7xchGHt3n3vyvIV4uwi1t4Js8ROHz5rfMzAW8v9Ds7evcnojcS0cuI6EvWzHsqqCPiGWhf+LehR2DyKexP0oX/jhepnJVOaVGFLNIwNqPFGx0hjoU171QWffh1kIUnNk8u3S6csPKczY1grk3btNeh5Zb4cuk6zVsAIhEUuhzdlnqRh7bl1TsaWZt7Kn+GlwC3NG4er+seRt/ihyblomlf+Lex+JbXYu9hX7LzcRUR3aT+vqwOjyg9jYLXA/h8Zv4oET0UwIsBXDMz76mhEvEcqI7KqlPzigDiMerB6PCqGeU6MEGNeNy84YG1p3VIviTEN+TJ6UN1cD3K0B16TvqUzNPZBHPszq3XVPrgX5Y0fZ+Ca2OwEV5Iw30wi0lYuTAGW4GM48gJlU9kgw+Y4rxSx8iXrXQ2dgedPjaMI761cKDw5F7ozPxh9fuNRPTviOiqOXlPE5WIC1g97/6BSPu/2Y6sZPabeSDBSEfJpXNaOXfomdVOlDGBVwugSSfKeNVk0he+LSCO1BjsZ+wUQtpKskhHUJrEM/Upgjht/ymZTu+aqG08fRvxIPHdUduJHSBND2UmURDaj6yIdHjpOnUWomXru3YIe/WrXwEw4eg7fr/chmeN3b80JvdCJ6LPAfAXzMxEdF/03xcfAPBXU3lPE5WIC+DwicpyhpteeBGRU5PqCKnpz9yCnOyGP2J7tRhHurl0JeO2SWwNndaLI1bEEqULEUUNkinD0cn+PRde2fpatM7UZ695cWbbxpahyDhJ1+alPaQMkwdAnI94eGaGyy3IXduOjb3Hps9Czty8vdC/CcATiGgF4OMAruM+dMjNu9MKroFKxBksn/t/9L/o0SYAOorXGGtXQkKOkSIl8sjN4RwcKrZ1vtg1sog+nQd9ZWtwk3QEcOOP3O1ofQBl0hFIyVzHTuG4EfS1KJ1cHfswwph8hfDE7ZBcSyC2PkxMRrf6xanqY+K4I8JUtuIRLI3lB1kEK7e2g448j+PFMpbP/T9wxWP+e9qUe4CT2gZzai90Zv55AD8/N+9ZoRKxg9Xz7g/oEwWilVgLYMHxp6XS4baXu5/vHcC5QzmtzNr1Dg7tAIB6F2U0ys3rD2VotCF4PtqCM/xq06NrpbS+qg4bwdYtjPySo4uCrK+HkjdOuqxakzoPurHf1W7SPrhhbPoQJ6yuuVH6THF9lDuhKEP429lHWciY2wZQPmVrZ/W8+++vi+Ic+bNPG5WIHUSfe2bxBsuGPfb4I62zDJ+qdqP1QR5GrE0q6/fABZLjk5jGzeW9dOI4j5c+EKkzNGmDC8WO0HLpUobUd1dICFy97IY43imZSXf8470uj/v/Wv2hzZrUTqNIW5drbekyAgFHbgy7WXzruEXUi4SXFLsrPJ3SntJninkr5S4qKhEbHF/+KgCL0d+bweAWMP7b6PPZ8S0P8kDoZDb2GXzG8h03TP5IZ41tjiOpTLq2JWVoH2+ki+ETXde1lD6UkYmUmOu2mJefEt3oOkvp6lojF0LrXGcYBWv3xZDOBD7207WtqIyOjatBkW2Xc5XERDu4QXLPZHjRctvg+PJX4crrX+3rnSUqEWexE0//oaz3Pr78VcMa9261QLc8Gnx2AOLPR/Sf+bxa+BMmQadT8ohUmMBdg255lPo+Q/kyGZOUu0566JxDkHzo0NyGuts6Kd0kXdvW12nkawTkj2ZsML9MRDkTcYnc2vDS5T5k2kPf42gSbI30brWAfVa4o3CP0zoB/Qu9Wy0im9G1DvcpLVe3f7c86p+Z8Pz2A4o9Ajv3eMbPRcHWI+JDWe+dPLjhIeiOj4CGQTSOqgAMn1lDx5SRsdXper8yQ030RZ+u4ndWo1ltW8oOGD7vmOandxTZ0Z+IZPMbG8nnZJBlPzNDOhW+JkqYYzdXpyR/Jp10O+u2MfdW606mBzvM1LepjWqwkQ8Iz5ZUtVE2Ja/YDflZrieno8sB9ndkXJFgF66Jg1nvPXQ0O9KU89oWXUwENoIBiDfIsaPK5VFsw+b38rZNPxnn7CexVnpHYL3HsIzIOHwSmxWCnm4iK4xYovxzMTUCauJP/CjdEpLWjya2HN2Oxp0vTTTDWumKjHtXRSwTsuWucfevGGzqvFoHGMhYbCThfPqFsOHL8MRQXRNZ7MI1ce7Xe9/281+ThgMZcEf9toQZveFz6ljtjzAI1WfsMthI3BxB5u1DAWyWbvamYL0/gv4c9/axYIpteHaSRqAk/2Cj8DPomfwJcu6KYCPJa10JQsCeHbFh7hnCfVk3nZeL2H0gslVGFuRo+y1Vk09z/ZJcLQadEni1wG0//zVFndPESWyDeSjYxYjYay37Kt56vTcRXQ/gegC4+uqrN66sCwa6VQOs+kksPYE2dpj+P9FrjrwJs1GHiKMN37UOhz0jkmXH8vWqZ9ZFzghLpp24WpXulRNdqio70u1ojLt19JMmk0nFTAxvqit/hP+dLN4kIICkDBtHLMuB7aTb0G5qAo1lnwgydji27enG8cOjvp6YkzIHs2rirSTr7ZJyP2gdhAnEpn/+AEdHuSwU0e/NqNj7YqkYsIsR8az13sz80fD7jQCuWHe9NzNfZuZLzHzpjne84w6q3eO2n/uayNXAqwbd0hmRGHTHi14vEy7ETOjCxt6WaIG+83SrZlKmOy+zTDCpdI7TJU3rD2mKPBLdUOZYiaDn2VATL13YCSwnT354tOlOzji2kraw5ViSA9K2DS8mm6ZteLq9m0Hp6XTdTvZ+qns6JeM2PCuZESC3hG65QHec/2ob6rBc9C/mwfVEuO3n9mNULIs61vm5KNgFEQ/rvYnoSvRrtl+iFYjoc4j6YZVZ7z2Z9ySRfUDlc1mTDJD6hnXkQUaHV8oOlE5HA/FHo72MLHpZyKy//nyTdPtZ56WXdFdNTDQl2xnZNp+UkR1xIZj20fJEFq5BE5HVz9mYTO/SdABxmabtSjL5yhncXfZeC8mvlIvI0RnsWFeSwlmTMQPJM1JdEyO2dk2c5/XellD6RPWgc98J6KjNTlwBGDfbsRNkg0IfXkR6gx1dVtilzTu5ediYx2zwzqumtxdtcUnjQgN7np1dgCC63grA3CnNpdOdrX0rn8JUvtyijFJ97cIXKUe3i9aVLS51nToa2pi7ZvzUD/r9S1Glq4k5hBdUIoMQeJN1HTBTmt/TEaIOz6eOqogmD/cB1TWRxU4WdJzX9d7dJ64ALbqUIA2GfSb0Ig9N2MA4gVUKddP7VWTyR3tR8NhhsVL7R0ykAwBWi2K42pBuQuTGkdYa4XFRY5V9k5yTm/awSOot6ep+bBuixqt4NCkafDwSnQ4hy6UPeQA/7GyVlwn5lsLW5EUya8Of8AI5882BOH9vKy7wyrq//slrAYRPOuoJMCEj+wmqCNkNY0NPqFzQYSbw8qgvywnv6ryNg+STNpCmTpcyvRG7F4LWqY1/ktA2INLvuInrqW3Y+lsXQfh9IEpzrbk2jvKofMPoT78IVCjakK4n1DRklBpkno0INr0jvy4yklbXENnX16tcE+SEmmnC9PJbnai9jC15CekJx7/+yWtxux/6bZwZKhFncSGJ+GM/9fWIxljcT75RE3Yyy8XIyuBr1fSDE0NGkW9P63jy0DHJ5meMURDmxcDBz5jIGP5IVaVHfktlP/I1EkBdTIqs3R26KTLprh4wSzfJk8mXsyl1Gq4rXBMQrlVHQQghdoh0R9cDpek6DaMdGjZmil+CA0Ea2ZBPyTtuUrvGNRLpAEgiT4SM1cpBu3T8Yz/19fiUf/bypE1PHhfL57suLiQRR+SDsZMMkzEh9CwhKqULJrDsN6A397E2JQQrd4pHlzmlI+Tt90lwym8Qp7c06utrLaTbqdpBV9VHh3N59UiO9rEeh5l+wWSvCWPPLc+rHzCGeIWN0wEMk232FI6ujXWjl55ODyFuNl3Cx5L6SR4Ayekc6mVgo26sHEC/twUwuiYcPXlW+ThewBJds7J5Jqg+4iwuHBF/9IaHDCduJFC+V+4QYoEL/mPxQaoTOObo2GD+4cglTybnlNm9ioVAFl08EpMXiY29telCAo6vVuqTlOfpbnIahwOXsC1pkIlZNoQT5fF0AQzbScqEnSGsbLr4j227GL+ybJHZxyFTfA2kTucIsmHkm7QtxvtkvnasHoBhnwnATNg5+OgND8GnPullvr2TAvddoMLHhSNibhfgFqCmS0gs0e36GXEQx53FTrR1sqmPrzfoKkKOyuGRGKmJd33T/ulhUlFNTNn0sd6OW0TKt2lI3SDW1aGvMzsRV4gCWBcu+Zh215NjOh1QA2pvUq5VvlplPl4sQcOEq46vJmAg1GEjHpg24T6edxi9Oi81d5KTOCLf6KViJuxyeh70y0TX+TTBOOPR+J7jQhHxR/71w5RLYNFHFoSRpkdEgmECLmynmCUi2RcCQBM2+LH29KcwNV0iH8hOy4Kc2zCUty+EkD7k0YRlCMJLS3QL+Uvp0fXqes6F6ajsEYwuK5d38BE7aVrXSwt2e59yegpG79tP94EY3AOtWfasSJRbteJN7Q+hSbZTS+jzW4P27ofUHUKJPbm+bhkT8Ef+9cNw+3/xUtf+iaEScRYXiog1bCQEMJJnpKdGzJ2arW4y+wyL7TZERuROEe7aBuRMpugXRXSWWpBJJ/PsDnmisuLO2qePafEXgbenr+nshXR7jels0nwkRwMpu8mS4on0XoZU19ULhKjutbUb3QM9cs7kAfr7rUf5dnlzp0bJrk6ImIjKsHoKbEjd5jl9pG1dMeLCELEeDScIz2b7iaNhks57sLVupyIPksiHoCcTRNFsvNIZJnk8uZo48qIn7Kf7GDrlR0/oCZ1BpnR1freJ1kzfFiW769RFrq9LwtnGtomiUFSImeSPVjfqSa/hOCmZTBttJXkw5kvK0o+a0Rn0rEvB0evboBkmAZOJVK3X0emOitXzXJHiwhBxp2I740/+WK/3H46xtpRZmZSNiijoTUVXJDZkwkb6IKn6crgmJ5rBjbYYzpeLr3n8/I9t2PwDvLQ5snWxbjkhLRodkh2hI0rXEQVuVATidB0RoW3bEan3UnOjIaxcYXApHMcE7OkR8eDH9idVMZCxJu/k5XTSqEScxYUg4g/+y0egX0Etk3RpD7ez7rGvLuTxiGFwcQgh+6c053S0HR09ofPrE39zsjnREzoiYGrXNFtmdC1efsefm/NxZsucYc9GNSSHuNpoCYViVESbSVfREppwo4gILWMk9Ymu0UZSOPl7PTsC9smVW0LXqWfVTOh596BTk3Yf/JePwKf/2Ivc9to16og4jwtBxAKWCa/gCm4W7SRZ6DyNiWiwegD6PSVslIXRGVboiY6eBGMaIx5sVECIhgD6l4POF9k0+kk5XYMkesLk1TaSuCOmYavPNIOqU+fb9JCtg7r+qEw1qow+0Z2Jw+FLKNpmkkbfro2WUPWP0onjF7Quk9SJGUjzeZESWbeCup40cmV8IXa5qA0H3BI6M/l4dv7iCouDJ+J+NGzexrKaqTsaVsY1TbrHg0WnNlfx9PX/+pijxDc7kEi8tFrLNYno/GPexUj4pmzrI86lif3h+kx4Wsmml+61XUJMBV2dltOdUxfPt+7eU9MOto0TG4b4tN89G8qG8EJUy9C1zWHZMlO0jFpfn35BDOTr6Hr6rWxer78SzP4YpzUq5kJI/kXHwRNxCcw0LJNt23H/hOJIIUywiL7rZlBuAwYiu0Mnl5eB2pyHFiZkLfh7GfD3mLAy7bc2+t5eEuvoFvPb+im5BnuzRhZm34bJPSB0Hv0536RtXcpf0k3cBErflil1JzV6HsgxkGdE4DZawuhI/kSvgK4bt/vUONMRMCN1yVQMOHginu2XktFRF9x0lmT1LLhkUQs+gIzvN+ihA5h8kh9dEmrxiK3/qiBzoh/kBTMr3ctv7GpdnT9K177bDTt9dLSRY4ud8C1LOgOhqesZ3RP+hjkuaXl2dXm2jqrMcRLUf0Fpv27O3ZWc7mxG0nYjInt6dcm24LT8tnxCe00Q0bUAnoZ+EuhZzHyDkT8KwA+HPz8K4AnM/MYgeyeAj6B3Vq6Y+dLOKzgTB03E7/+hbwFwNGzm45KYweAOZerjcgkJAYp8+FXH8RLchRqiN0RZmDhZq5NbLt3ZCRj7OW19qKV0lTZXbyg216m8cqc6YE7HcRVEYqMntgaSnEgD4ISF5dOHUXBHsKdHaRmAUR7az8pzOkT9NpnaLW/1onID+Vo3vnO6VUzgEHdL//P+H/oW3PEnX5hm2iF2TcQzT4H/XwD+DjN/kIgeAuAygPsp+Vcz8607rdgGOGgiFgyb+QAAMRqJmnAIRucBQmcPJx0n+YCoA/QkCqANO7kps4lesN8sUltTcplcEpnOR3KZFOcZlzLHadHfGT05Qy+xqZDYMfaKyOjkbA710CFoou+4RAiUEFuykEPZlP+13X7BxvgSZR7TAQyyRI44r8gBJDrMiCfgPL1BN9g05Jy8wJ0XbmciPk4Nux8RT54Cz8z/Xen/Afrj2PYOF4KINbhr0OrZcOKBZIqbkjChXY6TWZObAbWELoxAsrpM6FZjh/A6UWdC3qLVeeKucELU2BvBEycReOyMsgCkevI5nomUEHfOLpH7vOdQpt3wJ1e6rXc0khYbXlogY0t4Q2gbG1eHqWdnQ9VctxQG/+/w1ZXRAxBGy6l7yNOXZ3ocNTtultMC+y/JLeGdAn+/jC4AfBcAvdsRA3gF9Y3xi8x8edcVnIuLQcSZN/Ew+mjHBzN3nFGUxP3RR0A5pE3rZu0HnVzY2vBJ64SnASiHqFl91Rm9dG0j29m1rr2OzvG/roHs5JJxiUyGqqlrSMLd7HV56UK67Ie2AYgPWjX53DA2fZ1qpVyuLUVP6mgjWmJF56Wk7OfqYfOeNDZ0TVxFRDepvy8rwvQMuhdDRF+Nnoj/T5X8AGZ+DxF9FoBXEtHbmPnVm1RyWxwsEff+4RilB2HcQW3cdGfqCCUAY2wmjaFqXlkjoR71bo4mPd7IhpXlwrREnssX1c/pwKxISkdyeLpT+UtYRy87iVn4W6d5YWZJiJnSzbXn6MtNQ9W8OiTt4oSx6ZCyrmuy5Kf1hvqu4UIYJo+jCmZI3mnzE/cTb8b3txYm0WadAk9E9wLwLAAPYeYPDNVhfk/4/31E9CL0ro7zS8T7OHPZHl/Rlx0+6RtDqtkJIDXxBowb70xN9Omd1/SObm45HDYF8vRUhx9KNKPXqA6ZEe/krmsIHVfrFmwPHdyb4LPlKVuRvQKyUQZOGR5BJZNxGPu9rXsykeq0/5RtK0s4xnkJiK/du3arN5jJxRUDEVm7mxdlytGQF+3Jn2l3IlETwynwAN6N/hT4b4tKJboawG8B+A5m/hOVfjsADTN/JPz+YAA/uusKzsXWRLyvM5fDxFYIaO9ITXCVOrtBP8kioyaMO6IV8unP1miCbxhthf/bZvDlZXdhY4DCTE26sxoAd8c0jHXWskwHH5ZJT+zc5umO+uoTeM2lzRFKpKJk41LjlECsbLPd2tL7lcujJ9P8XddGud1RbVh4ofc8dj+4tfshHIWkmtnNY/L1de2JWyYP5/ibdwFWZe3M5rwT5H8EwGcC+HehH8lg77MBvCikHQF4PjOf2YF+uxgR793M5Xu/71FJmjwEMuEmD+fiKO9+iCIdODz43fjgCoHmJiGYCe0q1vfsM8fLdz09Zhr6k43GiDaPoTEdGO3ayUgvfTgNxNjwdjVLjvhReXaxI9uUPX+ntf7/zsoCaSUjQic9ZyOK1Bjum7d5/mizzyej19iG5J87gSaTpTnXSBJZEWWmkegnXB3v/b5H4U5P/7VsPTYG2wHEjsxOnyD/3QC+28n3DgD33nmFNsQuiPh8zlyGh3p1rHzCDlkCzptcPiGB3qFC/kKNKEvXRK6GrP9X7Do2Pbn+rO11QhHGr9lvt5kul+47sF2CPdpwr9+7vh0PqNaxJ/XsvNA14ig9cgN0apQZ0q2NsW1MLHJyb/IuE02WiZvD9RWPv3er8iKNnH+9C/tqlJZenzp275o4GOyCiN0PKVdxi5lLIroewPUAcPXVV2cr442Gx1rlHwTumqETDos/PIK1fkjjI3RDkKy/NOgXw5UUcduICLHZad9nyc+r/J+dHGekXSU5f7nnC9cjPVunk4SaxBrKL9RzSNdtqybMoqgJY3vQBYZ9IKL7pfIN+0RItsy9tPVMygciki5NCroLO8KLZXjBYMJF5NgQnNSo+AR8xAeDXRDxqcxchpHyZQC4dOlS9gmTSbrotI1ZM84U/c5hEYc8sHZlXl8nSvLb/RS8fEMZM3StnujosjvHbzvkd2Q5/Vkd2Ngo2dkUJXvjIofSS3W8jugeqcmyMToic82eP9qOpot1bvzFJd49yuh6ZUf1k3pof3M0wZpZoZlUYHwJead67AYns8T5ULALIt6rmcvRF3yU+M28o5DKxqSzmsM4KXNis8kHGN9vYWEHgHCcPfwXxwwdvXpwjiyn76YXRlClRQ2bINrP19hM9iz2vjyG0Swl6V6aazcjG07jMHWLXsQFV8JoZ4yOSEg2186aeGG+Tma5j2IdiYU/FfDu3VeHhK3vxL7PXOqb3y77VUmN3o6wQHqpLZmoobAIBEU7Nva0Wy7iPEA6ox3KH6IpcqPwzOew9UHmZFF6ZrFA4l+cMbnknRI8J464mMf5XB/8rp4P1COqkJ7YKLkCCq4bryz74veuYThJw+jmfL16cchGURKdfqZCHTq/rKzNHYBRXRMl7OSVeN5mLscJmXGvAnkwG/uJWXh4tB1g7HgegY72JK9aLp3Rl8kdb1Zf2xoJJy/Tcj2rr9PTiapUfyrNv+b1O2B/zaldr65T9Z+brmXjSzeV6bw5AtXXoc/Bm7UIxtnpr5TXc1vo+s12UVScGQ5qZd27vudxRbl9sPX+DOKH7VoATJEbY4poBPYMsCbsL1zqfNbXmhsB20kYP+oCyYvFRkPk8vXXyW56XJ/x96lohTSvTyx2kYItK17AUK5TqV5J9EImjyUuL190bBLHkRFDXZ0NfLx6S/7W+GdL5BmFw6kXtntmnamvrbOHd33P43CXX/zlrHwjnED42qHgoIi4XS4iP+za4TqRb9ds6g0UbSeTLTweZy6axY2Cgo1hkhAYYpRtrPKgByS6gzyzCbs3QehNNE6dabeOrTn5S/qzNpRX5efqtU4Zut0jv68zGcYdAU3sBij5eYff1Ut40J6YbIsmGdVL2nODrOuvj8L8TmClXXVN5HFQRNyfTODHTTZH7VpRARrDwx92vhrdBBkfc8YG2/PObN7IR9xvAgQg3tTH2h32yJjx6epM6qxzNP0UkvPjNsSmNta6FuXzzrZbO+FS4HFToOh8uyBLfLXGR521q4vQG//kiHdNDDabOEriRInS+QqpGHFQRGyh93bVx5JrItWHcFpMxdey6lhNM7ohSmFuNp/UI5c3p58uwkg3tbFuiNyknK5jTuZdU4n4c3an/vZsbFKPqevx6qDD0wTWfWLjhktlcIhXLulnw9JYFpgUdl0z5WVj3kO97bJmi5Nd8FHD10o4aCJ2of18IGAI1YofxHUfymHzFCfmdcpWZwL+S/XwSNmCVWf2dEqd204OeuXGPttpv6++ttLfOV9xrkzfr5wvt0TAOeR0ir78Qn08eM8OaP0RpFd2dsezM+DESsR5HAwRv+Ox12+ct+TX7Df5Cb9PjdrsgMRM/HBHoEVMzp6NqDMNhXsVd/JbPaujfk06ak5mq1jKZ5YTr4shj87KfvqQZuqZpHv5LEGV8uZsSN28m2N07YuEW/NcJAtK8ja0naEOBX/+phET73js9fjC5+xwx4FKxFkcDBEPnYv8kd5G9pD3O/ZuDVVO4RlLTtWAGfVmOsrUaqs5+slkj93MJtpZLZM/09HHXc7K5U5N+k3pliYO/R3pHLuF6/bkno6Q86z7on9VE4WR+2hOFIGy07WpWySpww6f/V2ij+jZudmDwcEQ8ep4vBT7ad9MRCusg+hTVZkVX6Dd93gdO1MuieF3Z1Is+zme7CJmPsvdXcw4kbtugGjXN+P+WGPWvWRHRo9eHb1yvLrPyefp6PhfqYutX9Tujn7uuiwsKXcZ//Jce5MoEPxJuRCqayKPgyFiDesra9s4PlOTZW6CLGd3qjxbVhRhsWa9bb4SOXt+X68jW3LITYpZ+VQnmtPJ5k7IzYHXTl77rSsX26XJPl0Hr33m3mvvf09n7gRqDpZoO2f1o+AkJ+wqEedxkEQ8heG4Gg+GmKeIukTOOfk6k4KbkvM6RFKKflinw0/hJEdaU9c59x5ORYJM3Y9ceZr8Su2wCen6k3T55/tsUKMmSriQRFwEUzJiiCIBwr7Fc4guZz+ejNMF5T97IxNdeSFApDtM6OgJu8LIdJgUy0c8lBYspBXIlzu1L2/WXiEkLb9fsHONU3qI23qqrrPJsGAjB/3FwzyGxnl1PtkwtM1RiTiPi0nEaz4Q0WedPb3XbsZFPHaymYTlkiVikp72Ma6x2Y759HZHXGo07L50Cj7FUrn5C1jDnjPqT65B6RRfnBO+UfdaTJ6pOzyLGMOzIgSevhCMzeacERtj7X53kXAwRNwu031Uh/PiThC2g3Tsj6aHv5tC6FrBdm55rn0RRHXJRXyoPFIHrUuNKrsxpNONOrqO2mau3LmI6tf1114qT+olerrdLGHpayiVq/V1uXPr7dmIQthMG53aqLbwzG1734rF4py9OE4ZB0PE1p3ATOg6fzLKi2zow9F2X6/kc9WObBx3hCWTok2zxXJp9DdGE/h2x2iBaV3YtnXael0yKZXJrY2IyNdL28vqOfXrVITIun5coG83uXelSdIpO9vCe7lqeJN12S+jE6hXRYqDIOI3f+P3u+m5G5+dNc50Wo+4mdNQtU0eNC9siAypSTlTs+g5ex5yq9KsztRkZKmskr0psptTpme7FBVhbcy5Nq+tbYSKfp70vduG1HITfsN1tHH6tsi1xZu/8fvxpb/1s1vbR40jLuIgiHgXKHVyG5LWZ0gn9QRzY4nn1qW0BFo6rCWaKffHHBLc9Qhm7ktiW9ubyHPt14W9Jbw47dILcZs6Z4l1nUnSgP2ZuKtREyVUIt4GuY7kETcQT+Lp5MyIG5g3khtGglrHTlztTYfcM+hJMvkbSI6jyo34cwSu05KJ1MxzUHFxcRBE3K7UqroQXubhNP10bvmZkQ7niBvIhldF5O11aJuWIfJNr39ObKvG3HuyTTxwCcVrzhGipGtyNW4J6xKaVbsduC7WxdxnX17quWd113Wo6HEQRBwtV11zQ+u5y5+Hky8K22aeBHJLcbnLk7d/UrB/XE6pc0yeVDyM+MiVzy0np+fZ9eS5+k2VkW2TGXtAeJvPnwpkBB9Ofp4qV6TrLDk/CdSoiTJ2QsREdC2Ap6E/PPRZzHyDkVOQPxTAxwA8lplfPyfvpph7Ymy7WuMB7QjAmkS/QQfdtlPbPQ+G9InwpMlVZW15JLftzms525YYc6PoKT0Pub04tsGszXwQk7l33NQs7Pj4oRM9QPQEiHgfuWcTbE3ERLQA8AwADwJwC4DXEtFLmPmPldpDAFwTfu4H4BcA3G9m3rWxT8d2d2uSExGv3bnW6Tylk4BjP3TJxsn11m0jT0ptMecUZN8XPKMSG7wE9mcibYQ9XXp3hnf/3Owj92yKXYyI7wvg5nAiM4joBQAeDkBf0MMB/AozM4A/IKI7ENGdANx1Rt4Lhc2IaNOyNsuXw66J5STbYp9e1hcDJxI1cTDcswsivjOAd6m/b0H/5pnSufPMvJM4y051Xv1eJ1HvfW+LfRuBrtNepzuxd0KGd3+K85lzjwYR/Q6An2bmG1XaZWaePLViF0Tsta69lTmdOXl7A0TXA7geAK6++upItlxeMVnJk8C+dey5OK/13hbeopfzhPNab8GG9b+KiG5Sf19mZjk25FS4Zw18AYAfJqK/zcz/KqRdmpNxF0R8C4C7qL8/D8B7ZupcOSMvACA0/mUAuHTpUtRgm86eb4t1H6x9IcCT6tC79wHuR3udFPaVWOdONq5lc3Mf8a3MnCOzU+GeNfBXAL4WwNOJ6D8D+Pa5GXdBxK8FcA0RfQGAdwO4DsC3GZ2XAHhi8MPcD8CHmPm9RPT+GXkvFDbyi67RceYeYXRq4VgF7Lot1jm+KZdvdp4Df4lsghNweewb9xAzrwD8IyJ6LIDXAPj0ORm3JmJmXhHREwG8HH0YyLOZ+S1E9PggfyaAG9GHj9yMPoTkcaW8W9dpRnzlaWGT+M1dLLiYE2JWKnfq+KJNyp6Lde1P7S8x6M2JD94g7M/m2+T6d3mc1zY4idHwYHvny9r3jnueqer2HCL6IwDfOyfjTuKIg3P6RpOmK8W5Cnl5d1KnGQdWcpffLyLS1dsXbvEwndYoyavnJpvs5FaPbaq3LnL7ZeT2epizEZG1fxKrLTdZvLLuxj3e/iIlbLv/yfY4mb0m9ol7mPkXzd+vA/Cdc/Iexso69RDLJi0eSpu0rNv5TotUo529VJneGXUl5EZsxZV1BVm0JeUMMps9ap1Yij1n4yCPYN09IDL5c/tKADGh7eLLZV2s+7ITPY/oS+13EsS9rz7xfcBBEPFqlS739YLSz/pBWGe3LW8zmRJB6Dxe+q53Ptt2t7OT0svpziXtqZeUt++1zuflOfvR6Drt3Ot2hSX0m5V/9v1vn3EQRHxWyBPrZg+cfnnMGanGLhOASJPtaLN2gDzGEWb/t21DIv+lqP+2v+v8ANCuu/9JZn7jvE8AnqT/+byjEnFAiaw0sY6dgZKJuKkJwk07Ul9+uuWiDoXsR3ZpXiETK5vjkmHe7YkN2t5J2s7JgfR6bdvYdpK2t3m0zU32E9H5LdowOWiJS0/o6bybln/aqAOCPA6CiL/qd5+CV3/lv5pWDMiNWOdEOBQ7+wbRGnZ/h2JUQyCEubuozYmvzu1mJrpZP+qcnb+ca9Eb3ay7g9lUmczkXoPVydVv7r2zz0nrXKe1tfaxUY5NXW400Zc7WSYTibEOcX/V7z5ltm4ZdWP4Eg6CiAGfRDc9QmanI7UuJh6JaJhTxra7nOXCt6Ym15hpOFsvN+mldazexnsGO/XbpF56N7Z1oiJKL+I515QL/7M6OuJhsL/DUa3Uo3XmToDsiWB74cu+qDgYIl6t4kspdZxdfxJrdKv4aJ0ikU3UZ50JttLZaiV9qas3CSUTWbnwvdzn/rZnqFnynRtJ4UUUTF1Drlxb3jp5cjb037aNqIvvR+pCOblnVtC2i5MbtdbJuiIOhojXwbr+Sa0vD+s6MZxz7Hu28n7N9UjX5plLbrl6rROFUYpCyMmm7Hn1yk2k5ch5brklcpbf17lPOeh6luohz54+UHaTck4bfIZlnwdcSCIuwY5Wch1tzmRXzn6OcOfas/Xz8pWI1+qVyMgLm1unQ82J/lgHOZIqjVznXGNO5sVrr/sFs4vRrb3nOl7ejqC9EfU+oBJxHheSiHNEq7HOQok5n7SbftrmRqZTZDDHF72tfF2UCHBbu6W6ThGAlz+30CR3D0pkPscdMifiY6r+OV3r+z0rX3Al4jwOkojtgzkVDbHOWWc50s35WpOyJjpcyR3g6QtyK/Cm3B1e5/BcL+t8Acwl8Kmy5+T1Rv6bhLFp2Ryy1eVpzBk558rSaJpuq5eLhp20axH/3Sy62fXeHDVqooSDIeLl8RXDJ2ips237kEUjThO94JY3FW5l7OTyJp/MzsvFCznLhWgN15EJ97LyXDhbLqRM12+dMDdqHFdIIWxNyinV3avDnBhw7mitEDtPX+eZCn3UaLtF8kxRszuXg25jIWrr6tgpcXJ6jRUjDoaIS59/m9oDEIVAbfNpLWTiPfQ5m1Nhal542lQdEwJS8pyMmYqHbEak64001+iAuRC2qdC7qWsvXfccuZQ997DR6GXYpoODZtHNi8XWXz0rv45EvPPwt12PXutkXRkHQ8QPfeMP4sZ7//RGeaUTlyZ0BPZTuPR31zVAp2y182zYepR8kVP1LH0+l/zjc/PlQtc2GVHpPKXrzLlNrI251+25cnQ9vK+dki8+p6v1B1dSq8g5s6FQ7u8ozXm2tp20e+gbf3DjvB7qOYF5HAwRz4X1w7Zdk31YmzUe4i7Y6boGnek0U3Yif3b4PZdn6MhMkU6JTKzO1CZDXj6PvEovgpwvuvT31IuwVI+pckuykitL2kq3ecm/rO/fXBJsQxnyv+RlprUm1jomwGk3ZsKiGf3AZxVRYftFxYiDJmJNOG0UKdH/b/cMSEZ3huzG/GOnmzNytnakU891dXj6RBzZ1STeJ4y/N0GXCy8drwMXZU6a1CVXj1l/5+ysUY9iepDJKFbaxsvTmHsIxM+R5Pcg7a2xcEjVXiMzoVVprfrCaApE6pEcGzv2ubd1OlGC5uqaKOGgiLjtmmgfiV2dZScPeT8aG21re962m9ZG1xHsJjN2xzXpDNJxRD9nvx1GbAWdwkgzl69kL4d2Rx1tUzvrXoslPYtVuF9A3me8UvfU26lN12ElE4tBf86eD8PzJl9vw8SfcmmsSaD6Ge7aMYJCP8+73kiIa9REEQdFxKtlHJaz7htej4y7ro9maE16Pm9Kql1Lw0kgQ7rUzd2QJpC9foEM0Qp6BO9HJWjittEOQxnuxj6xzKZnrzlThpVN1aWkM1VOopu5druhUqku+mU55ovlItM6gSIjsvSuRUpuzfU3C+tb9vMDCJNf8WITahiNE3UyF/qZym0ktA0qEedxUET8iLd9H150j6dn5XYU6xFXsgdAYWLNopPtC9Wnb+nsN8lTmvCROksHdietCpvleJEXuXxT6bm0IYRsImIiFz2R0/HqNbdOpXpJ9IN/P/IRIDpqQpOfrTOr50AmzEo7wYn+atlEE2xToY9RmUxAN27oI75l+2IZ8kxEsjzibd9XlG+CSsR5bEXERPQZAH4DwF0BvBPAtzDzB43OXQD8CoDPQb931mVmflqQPRXAPwTw/qD+z8M5UicK6WylqAFBiXxzduYE8ksoUsm+7mC5uun/tXxK5qXnXkJelITXdrlrKXXAWeFfHvk7S49z9Z86bsqrh3fNGkPebsyfu/aua4ZNfaYmzLyIijl57N+tuBxUNIUOnTt18DT5X2RsOyJ+EoDfYeYbiOhJ4e8fNjorAD/IzK8notsDeB0RvZKZ/zjIf4aZf2rLemShH9JVmz/+xU6mZeNIeYxayOW3dgbyVREVpUnA0sthSqf0clmXgC1RzXlxrbvz2pzJzlIUiEemuWiI3AvF2p4jK9Wt9DKykRGin2uH1rSnzZcrR+BORIZ+cLQ4Af9DAXVEnMe2RPxwAA8Mvz8XwKtgiJiZ3wvgveH3jxDRWwHcGcAf4wQgN1uTbm5kM+VDFiIVAl0nykHQmsNMp0KfbF0bQwqio+2Iju20OVlOv2SnFFGQs7MpbBiWF73g1Un+TqIuvPScbskOMESe2GvUE7pAOaICmB99EV0ex5OLkm/uXIh9fo+XVwx2BCdFznWyroxtifizA9GCmd9LRJ9VUiaiuwL43wH8D5X8RCJ6NICb0I+cP+jlnQt5uMYy5z2kOhRMQo9KvlsgHjnbjjhFvpp0S7qiZ+uRRlj4L5uVCVkj4kHfjqxcAjawZXrXtqvoCW1P+1uB8uiqVW0RfRF1ow92GKE6bSv2O8AtUwjR1kmj65rBozTlWpij67kfpI6RL1qR+ty4Xf2lcLzlXtIVm2GSiInov6L371r8i3UKIqJPBfCbAL6fmT8ckn8BwI+hnwT+MQA/DeA7M/mvB3A9AFx99dXZcq57x+Pxgi98pisr+nu7Bi0TOuU+a7yBoUOIU5/sOo+nn+vIVsfakUUkNt0rU/Q6RczaZVIaqXtugtzfJ4lcO+deQrkXWOcQsqcLIGljr+0EJVLO6VhfuPfVNUXKktaqieGmCSPmzIKQ0n277h2Pz8q2QR0R5zFJxMz8dTkZEf0FEd0pjIbvBOB9Gb0r0JPwrzHzbynbf6F0fgnASwv1uAzgMgBcunRpu29fjA/9KrN+3+p65Ntl5jw8Au+6JtEXvWhkbfS0La/cpolH1SJrmpiA25aS8sSW6I51Tes3BZtnW2xiT4jI5pFrt8TrpXs2xjYnAGM7xr7xWO6N3LWOlOETt/6rrJ/1LbeSr3+RLBblUflpoK6sy2Nb18RLADwGwA3h//9kFag/n/zfA3grM//fRnYncW0AeASAN29ZHwD+qHjwHa8ad9ThQUhK8nddfjY9R+CtCWXSumK/t1vYjCd0YK9cqZPNJ51Z2+06XUdS5B2ThrXhyXIjelufTTDn+nKjYGAkU63btoCN65V0IB0Jt5mwwzHPaM+SttiUvHKPtQ4ArFZjuYuF/wxGrpXVaFPsCnKDgl4Wv9xLxHxSo+GzWFl3nqK6tiXiGwC8kIi+C8CfA/hmACCizwXwLGZ+KIAHAPgOAH9ERG8I+eSCfoKI7oPeNfFOAN+zZX0GCMktwyKPtmuKezPkyLht866EXL5VJjRNj0SHScDOJ+lxhJYSkf2c9WQ+OZejIzz3hk73dEVv7sttClNuG6+uOVK2uuuE50m6/J9ze/S/FyZguwZdN/pvbdvK/8vl6B7y3AnxyDrsTaHC23LtnfUtd/0IVZY4Hx11RTLfFuzU5RSw91Fdgq2ImJk/AOBrnfT3AHho+P01gLNJay/7jm3KL+F4OV5abnlryS/adQ1atcQViCMYvE7PnG7ak+vEEtVh9XQH1ROIOZK0Mo8Qc+Qm0SB2xt4j96k0C01sUx2wpDN1nVNpwPgSnvKj23rrOuTIfiA2TjcEitwggTz15jueXhd0mwzBRoTMBAx5gUWT9wnbvAJZSr+aODxhF+DTD19+OPYsqiuHg1pZp/HYd383nnPnZ0VpuU18ADXKWDPofNUuYAdERP5GP71+k9WTjujtjTC8IExnasTXa/KVQtT0C0NsNsTR/hbFkDinHrpMbbe4+c5wcfHLK8pv9KJNepy8fcQLDdfQ6XZjmhfaBkQ2Svq2vjZfZFMR3sLZLF7r9ZEfQv7+hkEWbUdou0XWvq2rxWPf/d2TZWyOMwlf27uorhwOlog1cpu+CPHIqMbz+Xp5O/aPX3InXkLnLH5uBxKNR5EjUQMYNjOK69h3vnhkN6bHddMdvJzu6U3ZANZ/iVnk7OoX3Ria5qcLdD21bhyHm6bbfSbGkLe4vJUszDBL5oH+XnWgIZ9F/0Wk/b1TC3yALhBs06Qxx7pe4ws/Dq/Ll5HW70TAG0/WXUVEN6m/L4eJewD7E9W1LS4EEWt0TGgDic5xIwiYgWVwJ3guA6vLTGhZj359vfIyW391m+i1ai8F7dqwpC62vGvsd4TzSUD7rXu/Yu7zvNx2m6JUN6mHdef4UQg5X71vU/7Xbipg9N1GLochysLZ4lIvosmMaIcvMXU9HgZf8mqc62iaziX6pI5qNR8ALM5gifMWPuJbmflS1u6eRHVtiwtBxP0IJEygyd4Aesa54CcdPvudfCK3fsq2pUHPI3ndOTwdKxcdketVgw2lfmVrU67R6g9pqhxPz3bkOf5cbW9q1VhJp9R5bb1yadYtkdMrpfdfQfLZ3yVtqGW23javd212tZ/na9a63AFtuwA15U3orQtK55P6nFZI2xm4JvYyqsvDQRPxY9/93bh8x+ck6UQM7lJSBWLi4i4dKQk0eUjn7dTnpti3PuDWnnnWpLb0pIZ9YUS7uTXapxnrr5PuvWisHgB0jXJfOHXUPlktb/252gjtsM1k2ZZuD0lvQcm9HELYmlF3jl4pXcpbdc1AgFMyew3i0tDhaolO+JKSbTFLK+W6VrlBGt/9YOvITOBW8vcviOvf/9hsvl3hDIh4b6O6LA6aiIHU/VDSE+LVD3wyWlUELqFtU2VoPc+lISShQ+yG0bPpnJ4s8i2btFy6thF1kC4diXm6tv65Oq4LeZl1TmyvlidRDW2aBiBJL+WfY1en2zrqMlfcDKPfiMyDzvFSRUZkAhYkDNLqefe4DecjevolnN4ii3SjrJPGPkd1WRw8EZegH2g90VQ6mWLohDye1uFFOBBxP1rhOJpByHhwM6ziI2y0XPRtNIQn81wOWt9Lm9LNHcOUy2/zbIPJjX+cl591K+hIh6n8Jd1iJERBNpzI0WXcGWK7i8+ps2jDxvSiB5RfdKKPto+eOC3XQwn9PMJZ12J/cfBE/PhbH4NnXvXcxEUA9B1FBoDy1a0jFixWrb8qT0c4jOfDxbGnlowBDDqlKAobDaGjKHKhT2PdfFsdKNED0qiHltOjnUrpItM253a+Kf116uLVwY+KIDClZYtNDYmg8KIkbHSFRdcRmBvXpsh1lEXkltF15f7e9dfBQ4iaZ3MYea/khQMcmUk6KePxtz4mrfQJoO5HnMfBE7GGnBsXj36nXRaSR+s2mU51rMLacjr9CHPUsbP4Uj8tA0KHzeRrO4rKI+JBv6GxXD89vW5dpk3vvyCQlCdliM1uJgkLSvYADGVGEQ08pll9e11aN44q8NJ7/UUT25WwM69MuW+LiKxjmc5nMT6X5JKsXENvuJ+AXoTjkaZgY4x3fSbdHJyBj/jc4EIQ8eNvfQx+7g7PK/pypZOOkQnSgRpYP7GG7WgCIo5sWh3PjpVLfkmPSFnJBoJV5Oqld4zoJGdNlNq2tRvVM5Nfn4y8KaL6ZHaEs9dg0+Q67TVN6baDiyGuy6pNdbU+gKRMeXY8Mh3cBkau8/fl5m1ovbYjLNvRH71o4peUh2XbAG3fD/7xX52OG5Rxmv7o84cLQcRA3HGsm0J31rYjdOz7bTXGDjyuRgO8sKe8j1VIMjdBONaHknzMhKUibhmHt2z8vuhJXexo/660hbYtNgTeKKY0stnlqGfdcpgpqjtQDlNj5ZcVv3FrdG26HjUPJ2gTmxdDIMI2Xvachryly6KtjXbVP7e5lXXWH90Gf7T+uvDud697isRYfcRFXBgi/v4Pfzt+9tN+NTsRx0z4xCo/629JLDdi1Hp6Qq+3GesIiWsMfuCMb9fLp33HdgJR2xIb4zU7fmtjd/B5O9dh9a1sG5TsrltHIH4Ri43eFxwrjulpeaJvy/P8w/GKvXTJtS5Ly4F05MgMLFeLxC/shrN1NKzCO1rkY4SZ+z5xmqiuiTwuDBFb2KXDuf1vLVn1D/qoq/20Wk+2LNR712qdtov3CLYr4Pz9g8cTJUp5tL85CWNj2yFSf6a2u+qcsDlO7er2svbmorSqbHzBcJLuhZH1bZ2mxdeRt9u26daUos8Up03lARBk6cSglgOEo6O0LUed8ZnxJg01uq4/bUOeobPwCVfMx4UiYhkVA+Gh7mKXAxGijdKjSbIuHW1aeHp24/V+W8t8eX3McSwDxo4u+bRNvX1hnydNF30vTevm7PYyP118nnbf3fWhXCpZW+MLym6gnu7P66XlbKRpq+AWsDakfRaLOF3nWSw4acdxP+HUJhBGvsuxzOykXts/v1bPlseM4ZlZrvqJPXlRnPZo+CziiM8TLhQRA31n0PG3peW1QtYajQ5XC0Qp5Jn6gdUoucOgk/iRB1njygA9697b1en2ZdK2cTlSV12+TvPK81wD0ql1Wtxe2Bn8jdzjcpKwtTYdcdpN3KUNmNONfJarsW10aNpyRSp6ok/vmNCt/Mk0Zr1gw7m2bix/fNGOdsE6QiR+KXl6EmWRO81k9COng4/TAqP6iEu4cET8zz72KPzbT3q+KxPibNswSQZCg5HMPH3RFXjkoXXiRRu9TklmF3QI+s6cvkx0XbQtu+dAA05GKKKr66PtpgtL8r5pbXMOPL+otpcsGea4ftpnK+QY2eTYRytEZu3m0ldt7McVeOnDRJsTzpbIMuykY59FJ/cMAn3YZNNyWMCRJz1m4J99/FG+8IRRfcR5XDgiBjAQrLcQwxKOpwsA3GFYGDGQhdHTAfieHIi3ZWzCKMnKSnYlj7w4dFk6vVG+YC/dS9NkIIgnoVJ4Cy62Qak8r34AsOya4fr1JKW0YTQK5lhX2rBlwkL5xDvQMPPvLQDR+lEe9C4rK5M6yRyF1MHKAeWGgD8hOJQDGnQXxO7CEP2MnCo4fdlWjLiQRPzk2x4ZjYqZ+4kNPQrzRqG643lyTdpWhyiWe6vbOhAa5kTWUZ+vZT+PDg3SLxCd3tHY4Vd6BC4dU32Ktxg7vq6LfuHkVueJXLeJbjsLbdPmsfl0nbTMq2tHpOchAQTS5p6ktI0WlNATM7Byoifa0K5emaLv1VHLvHYRuRC2lev6LDJfGcO1dzKB2b8A9DU8+bZHunlPA2dwQse5wcmfj7Kn+OHbvg0AsOoIx20Txevqhx8IIxdQpOvpAX1nX6nVe56t47Zx/bNSHysT4vbSgdjXyzz+eOmrjiKdwX4XT36Jbs6GVxf5kTYQPdH19EUu+vKy8fRFz6u7pAt0ubZ+ORu5dtFtHbWXeRnpuliI7LhtXJIF+i8SaQMvv+gct03ftqCsLfm6E11gfObPAozeNbHuz0XBViPiOaekBr13AvgIgBbASjZ6npv/pPAJfWxRQW/V0jC4Ej3tf5WJr2E/g4yOrJbK5c9N9rVdXz456Rp6VNkyRfq6XG1f27C+aUkDkKR7ZbryZAUhXIyb7vj5PH9vImfHV2vsDdfl1Js5Hm2OCy/6v/tVa6Osbft7YtMlj5340/5mz56uB4BhNOu1favKP8qcAB2aqn/JnZVLYkCNmihh2xGxnJJ6DYDfCX/n8NXMfB+z2/46+XeOjjEuLR3S1CdrR/jEisJqtVhPY9X1q9xyOh0TjtveTstxWV0YPS47yspaZVenc0jTecRfafUl3epqG1pX0tqQ5unrenpyyT/nx8unbaMgz9XX04epl9aFSbdlrjpPP04f7QNL+SpwZC0Dx21KTmO7jvkttE7L6J9R9UWgyxK9dff92DnU18Y6PxcF2xLxw9Gfjorw/zeccv6t8NTVdW56y4TjjrBiSzr9/7rTpgQa6yyVHSidoXNzvKQ4J9N2pSPbF4lOK6WXdFfKtqCk79Vnm06vbegXlyWVjuOXmshaRtTenn7OxlS6VxcgLpMRX39JJu0tS9W9Z0gIvXXIVf4WO8cZtwaQf9ZPC/2109o/FwXbEnF0SiqA3CmpDOAVRPQ6Irp+g/wnhqeurkuI6DiMbkuEctzJg+/LO+51cqPpvoNNy4Q4xaYmPd25c6Sl01jZt7pSpkCTr7WhyXfZpTIttz+WZO2PZ2vZ5eW2jgLJA6dOyNjwdG07L517Ktek76e+pzqfJ2vDs5JDG57Jko7U4di8+Ds+exIW1BFxHpM+4h2dkvoAZn5POM76lUT0NmZ+9Rr5EQj8egC4+uqr18k6ibGzi18UYBAIagc19P44T69jTnRY+eSsnb5zkysDtK+5Txl2Wsukrzi2JXWTckS/txFfm9Xt1NOv9XW5Wr+vLyK5zi/1LCGnp9uw5d5+tNOZqpfdHU3fl3iESq4dz8YQx0s8K53Rk2DsZx5l+hqsDOjJmMBYqGchbuewjJpSHW1n2RGWkBA27A0u0uTbupgk4l2ckhqOJgEzv4+IXgTgvgBeDWBW/pD3MoDLAHDp0qWdviv/1eo6PLn5jeJ0Rj9yGSfNPDlg4oKH+aeR+LQNS/g2ryZR+XzdNL2vGxT5jiTW+0Rj3d5Geh3aTrLfsmmZZWEEp9EOdZrSs1Q/prdOfca25qFufb2dXdqCjSF2WOl3TGunA74tBoGcckQOxPWIZT1Wqq6lNlsx4cfbb80rnDLO3E+9x9jWNSGnpAL5U1JvR0S3l98BPBjjaaiT+U8T3giDQcHP22+FmNNredTRn7KjbcKxIhI2+XVebVvsaptC2nPTxZ7v9xxJaY7vVGReupd/nZ+SPe2P9dLZkYm+vGjitLhtpA6rYbJuvfT4eZDwIDIkHMoHwrMQkzCbvK3REdtQOsuOhufU1mGfsIlb4iK5JrYl4hsAPIiI/hTAg8LfIKLPJaIbg85nA3gNEb0RwB8C+C/M/Nul/GeBH+/ikcPoczOTZkE+dAgGlpzXEVJaGpkuZ2nyatttSNM2pbPOTdd2tK7WbzM22oK+lbFK7zb4aWfazdUpdx1WH7o8jtO6jK61Y9OjyJYgl/uuX8j6/st970wdJO/K5M/pHKv5BA37TJ816mRdHlvFEc88JfUdAO69Tv6zwo9334onN78BIHQQJWuAMeY3/K07oH2jic6Kx9+BMb+g5VGm7Y8LQGRzd1Uuz0+HsoVQ53E576grZWndqXSR2TI05u5PbPPL10d2jw9HPix6cNKXGK/Vaw9Pd2HaUEg6l67roxdYHIc83vV6ZWnIS/QKdS90fvn7mEc7wP6RMHCxRrjr4kIucS7hx7tvxQ/Sbwx/53hkqUYxhJHUhEylAwo8eWtkQNyxSctDYceZ9KXKo9O1LX2Kh64X1MsgIoNCOpCRWWzb+XJ1CGjYl3np3rUyAHLKaNGPMG16V0i3tnT6ioEF4heByOSF7slDlXHMYfFI5oHUdn6a94+EgUrEJVQidtBiJK6I3DCOUHiGjkB07CjKlSk7loxblQeqTP1867pofVuGBiF2XTSqXJsOxHXS9bU2N4Gtm30paR25Rl1fdtI9m9qOuD0WJl3IjUx6Z/StLc7kkRekJ5P/F4HIbR0H0g6EXdKpOH+4sHtNlPCzYUShR1PSKVcYCc6SG6Mfsa7U3zA6kn9KJgTI6m8vj/arWluWBHQZ2o8s+aH020y6lum2KPl71/mxdqR+K0cHSm7bwF6Xrpe0jbbjtZn1WUu6vSea/PVzkrtfOZnU4Rjp86OJXj8n1s7P7utoGNVHXEIl4gx+RpFxi/7T/xjpJ7Du0EukZCDw5LbTW4Jhk0/SdMe39qwtPbpbIu342gYj7ty2HE/mEdYufjxy1mV7dYexkau7tGnuWjqj65UDjM+FTbe2gNSevhcaVu6Ncjv0z6LWAcZndl+xyXOwDYjoM4jolUT0p+H/T8/ovZOI/oiI3kBEN62bfxeoRFyAdPZjMNrwWNgRCDI63iioJPdGvEA8utadLjey9mxZMmqNvrXfOfYxIbM6646MvZGwhm5H74VlZXbkqtvCI3KYNDjpK3ONehSLTB5vNA30z4J3D7W8f6Z821pnF6R14mBMhifmQha3wLnZC6f6iAuQz7wn0AuCT47RYFyQMX7u90+M1dHycXJnlLfh7w7jG7EJe+P2pDE+iZIHAJam20keL13QDXXs09joa/tdJn1K5ulsgibjYbZlz5FJepdJ99IA8QHLfWSlk7e/UG0bl5vL09/7o3D/bPt34XeRI6Ozr+4IDR3rfIp4OIAHht+fC+BVAH74FPPPRiXiGRBStUS7CqORKIpB6SyVXJOtdCIZ7ZDJK7YENl1sant2FNmodF1HGFIZf9cvCz/dk0lZnnxTlMoc4V+HluXStawN9DD32jmTXqp3G8rRk3tavgx1OAK591/LpRxdn/OCM1hZF+1lE7ZY8MDo98JhAL8YVvGuk39rVCKegWfydXgCvWD4e2mIT8/a685l5SPZjiTsRl2o37VN/UKQEbWObrBl6efec2F4URC5dNuH9NhG25Q6bAOphyUuQH+Wp/UYvxj8iAadbttGt6V2HVkbUjbDj37gUIbnG+7gRzzol4K9ZitvzLU9k/djQ5852JCHr9J+WwCXFVHuzV4426IS8Uz8QiBjGd3Ip6YmUPm81H49K9c+yCj+OPx9nJHpka10TE3INg9MniGG2LEjttpMemmUq+3al9G2WGEknak6WLnk9dLtCDj9wugh/l+vDm0mj9zfI6QvP21PnhVL2FPyZagtoX8mzwsYG4+IbzV+29junuyFsy3qZN0aWGH0B8vnoe5s/Wx3P2lXknfhRyZZZLR0bGRQstakw9izZUk9JD0XXaBtdSEPw9abhx82eTuT1/vRebyfXL7eZkxGXh4r13lhZPK7nrhDyLM0thCuy34BiXtCyu5MegcO93Jscy1bOtcFZbMkB/rnRE8QnhdMPQfez5Y4N3vhVCJeA78URiC2Y8hDs1KdnzNy/XBpuci0bSEVnc8SHpuyPH0t0ySny2idPGJLo1N5Rd6i3GV0Hu+nBF1GLo+VS5vq69J1blWdbZ6VkbFJty/ZlSlD0u391nUu3e/+mn051N+/dI5Gw4IziJo4N3vhVNfEmng2PxLfSb8OhnzGj2Qmc9uxSyGVxz7leOSm3RTS8Qk0pAPaBRLLRgKJ7Ykt69KASReZxlyZxaZv+HVszq1b/PrTI01OXABiS79cuqC7MNENWiYdSX8p9P9z4q8e3RhdYrNT/2u5tv1sPruTmLfBDka465V3jvbCqSPiDSAdQUY2MjHHpvMiI49jUGVE1f/TvkadTxOmHrVJHu3CsPVoVXpOf0oGle7lY6fcTX4Enu3W1E/rWVkpn063C3By+sA4Oh/bdbxmHfNry9CjW11vhHusbSIjPwQS3uZ5OHRUIt4Qz+ZHhk6SEoOgJO8AfAKjr0/LxU+o00Um/kprszUyTRKtqofoW8JBQWaJjZ18Nr9Hor7/uEzogql6eC8OFGQd7CKb+JrFx+69hJZOGcjkQSjnE2D3GZB66CibnPy8krDA+n/n/FwUVNfEFngefxu+rfk1EJP57Gcs1QkLVi4dnokHmZ75b83R7xoiI6Yoj9gbj4uXjpySkhdlIOlwZBZd5nfvrT7X1ty8up6eTuk6vGuXNkoiJlRbal2W+6pkOs8SwCInY1ncYV6kxMNXyxU8ure0/Hndo3DecZFGuOuiEvGWeH73qIiMpbNG/ltFuB1GMgWARpFuE2RCGGwIWfKJbDg2PZNHpwukHtqWTrd5SBGDyHQ+a1dQ0tUYXj5Bjwz5sT0yKKM7XDeCj121jS4HJt3mifSJs7Y64oFwrS0EWWfyIBDuIltvBii9rucfAAkzULfBLKC6JnaA53ePGjrUirrIt6tHNUyMFXXJJ7LIlzR+2lpS0/mikRTpWX4kLwPrRhBbgkhfkad+abSh7ulIMtVrg/2Sbs4H2IX207Zy+rrOWk9kus4634q6mFAxuiU8/Rbj9UPpMziyBWNrafLAyZeQMII7K9yfQyFhQek5mPN8HDLqiHhHeH73KHxz86vR56+4Kbow7x3vHSGz9jR0ROve6BcNkJvPbkyD8HndHx/afyJr/V6XVDnxiLhzygEwzNnblYIa47w+EvuboAEnNjX0KHrc3yH1qcuxoWk6D+2kbXamDXXb2qsZSZ/DnhTWZxxG05n6ST5PtgLQUYf/0H07Dgl1QJxHHRHvENJx9MgM8CfZ7GhMLxrQo4FxgUgqsxOB/Wd7XI6epNOTdtaerp9O14smrEzrLMOP3QFtkx+ZnLJlr1u/3DXpSTrdRtqml8e2v752r1305J13nXrSzz4zh0bCFWVsRcRz9uskoruHfT7l58NE9P1B9lQiereSPXSb+uwDftMh4yXGT2jb8ZYYJ/bYkO4Koy/XuiukE5eiBwSafHJ5bISDZ89GP+Ty7+qfVw8bbVGq69Q16peUbX8vj/4qEBnCPVqZ9MHVQGkEDKvnwb6A9TN0SKjha2VsOyKe3K+Tmd8e9vm8D4AvB/AxAC9SKj8jcma+0eY/j9BkvAwdTtARok46REioOaqB8IxM+x87wvAjMungki7Eb9NLeebIdD09He8nh6l8+hpy+azOJrJcu9k82o+v701r0q1Mv2x1e4jskElYUIk4j219xA/Hevt1fi2A/5eZ/2zLcvce0qH+3uJXAPR+T6D3GfYdcZxkE59o1EEHWfibRn+jlul8Nk9OptMtwemycjKxLfk7TL/RW5pQyKBDH+hgfa3r1tn3YyM6wnkgV1NmLk9EuqGcBeILHduHozbQZQGMF3ff4V7XIaH6iPPYdkQc7dcJYGq/zusA/LpJeyIRvYmInn2SR5GcFf5z+2jE+wzIZ243pNtPaivTn7SlfONeDGlZAptnBd8HKwSycnRE7vlSd/1j62zL0vXz5KJjXRmCJdIolg6czdOF9rRlWZnNtzIyXdZ/bh+NQ0d1TZQxOSLe0X6fIKIrAfx9AE9Wyb8A4MfQ36cfA/DTAL4zk/96ANcDwNVXX71O0WeO/9I+BsA4Om7DI0ZqI/AmRE+06vFrwViEd6WVdRhPfegcm91gcySFca+LuKz0gafIXq+TriQTQiGkw93cKRtT8DYBGokrb7NFF6IomsSG+HfHthzdBEDvP2/UvfDySL7BrWRk+t54ZXk2LwIBj0jdSxUjJol4F/t9BjwEwOuZ+S+U7eF3IvolAC8t1OMygMsAcOnSpXN5RzswltShGVa/KXfFMKpFkPVo0SXhVGMIGzuy0WaLLsnT2xxHZDrd5rMykXt1sZjakW1dyCe8Di/zdTpXnnvliF1GHNKmX3leqNtcmVjXshW1uIIvXsDSRRrhrottfcSyX+cNmN6v85Ewbgkh8fDnIzDuA3qQkJHxg49+uU9QhNz7GMOxPc6S6JKsk1Vy0eq2MR0IpKuX3prFBqOMCzLfLly97aHL0eU1nNez8jltULIr6cnS6GGV3HyZlPeK1eOcqz18nMvR0ylh29fynP0+QUSfEuS/ZfL/RDjG+k0AvhrAD2xZn3OBV6we1/vMhtV4/UgZwJCuZWMIW1gCbfKxI+vC8lz5gbLbKhnWkOXknt4ufizsdVo9Tz4l022k205f55I6rBwZgr2czOZjXGwSrj7iPLYaEc/Z7zP8/TEAn+noHf5UcQavDB3yQUe/jNXw4cw4GpwLsu9E/yM+1+EEaGKs0EW+TU+m0wXWh5o7ydjKPHlOz+Kk8+3imjowjqhx8y3RuTK5N/ZjILqn1Az3+yLD7hsyL9Pu67GPqEuczxivXD0ODzz69wM1CIEKwfZ+4JRM4g3n48km738hH/HdSnk2H2dkXl5k9HQdp6B1cqScTr7JRKZPqIz4mq1M501P++iSfOzIvHaRdrCncVQS7nGRRrjrohLxHuBVq+/CA4/+PYDx1A99JE98UOk44SbpmpBF5uXTBMVKl4CsTPJrurL0uquJuXXt6Lp7MnmpWHmH+IXCJh0mPVc/nU/aztp81eq7pi/kAkBcExU+KhHvCaTDCiGvzGOrCVJ/9sroGegfdpHZUWoDSvJpuzmZZ9fWy2LOWXQ56OuxNr2/j+C7C+R/kedknl2dz17PXFkl4BQ1fC2PSsR7BunA97/icj/CYhvDy4M/UmRCnnopdQsG8UiUS7X1pbY5fLJTn67Tok/sUGbHPiFavU0xnNM30WelnBXnx1lMvlzyLtENewPrdtUyrw1WnLoomPrfX7OsBJxDHRHnUYl4T/H7y+t7Mg7LaTvuiSKahAsy4nFiD1AjWdIxw2pCTxG5EEifgCi/ICJbJZozct0UDc20TdP1lReaJlp7zW5+QpRveEFRP/K1st9fXj/r2i4i+giUOiLOoRLxHkN37PtfcdkdqQIAqF9CO+4t7E9UJTKKZd5EVGkyLae3C9iXSknHXq8rpxntYWQrMI4y+Vbo0BBV8l0DdUScRyXic4LfX16PS1c8Uy1T1kSD6H8rk1VjVqbzAJr8UJSXdDxMBauX8nfqBVPW9a/Jk2sdb9Oi3DXbfP9j+T3Z2lSk2MhtdUEG0ZWIzxFuWj4eAHDpimdC77sg+xyMI9p0R7FxebPdOc1GWJAhqXRkqnW07RzaonQado+JeEmxXB/B7paWk9v69tfp55X9K7RM7kNFxa5QifgcQhOyFyUh+zH0e0bERNqABoLVMp3XygA7Msx/2musu/FPzr3RoZu0pdvBRozoCU17Pfo6F2iil8+Yt8URmkrAW0DCIyt8VCI+xxBiuNcVzxjSxkUFnUu0Qki5xQkaXrrnT10n/6Yo+XKtPBc+t0JXDFmzNuT/Ny2/d2fXcZFx2j5iIvoMAL8B4K4A3gngW5j5g0bn7kFH8IUAfoSZf5aIngrgHwJ4f5D985M6vKIS8QFAiOJeVzxjJFoyo9bwp5BwR3G6yDpwlDd3BnqH+KSJwcYOuDd3okfDKVHqPF2Ymz/iVEdsSujZGAGhbIBxpEL7KgHvEmeyDaacIHQDET0p/B0dXMHMbwdwHwAgogWAdyM9QeinTrqilYgPCEIcX3rlM5K9JoRwktGiExUQjaQNKZZGwZ7+NvDItjTxJ/qrQkiaZyO6Jmrw5uNKwLtG75o4dTwcwAPD78/FHp8gVIn4AKGJ5B5X/tzwu7cXgsUmoWsnhTlleDqe39rqWXfN247/8RY1rZiDM/ARRycIEdGmJwg9GsBNAH7QujZ2hUrEBw5LMF905dPikXJmUkvkWub9Lsht/LMtSr7gnK6dmLPyFTrcfPxPdl7XijI2XHV5FRHdpP6+HA6JALA/Jwhti0rEFwyagL7oyqcBSCMjPBeFYJOJuTmLMtbB1MSdTZPfK/meHbaImriVmS9l7e7JCULbohLxBYYQ0xdd+bTEZeGNmj23xpzQtXU64FyityNez3ctOpWA9wNnMFl3bk4QqkRc4RLVXa/8GQA28oCT45BKxLmrOGIPK3X2n8Y7j39grTIrTg9nMFl3A4AXEtF3AfhzAN8M9CcIAXgWMz80/C0nCNmlkj9BRPdBP6B/pyPfGSoRV7jwCO3z/sZPJ0cYeWTbuOFjPsl6ZOqfVZem/fnxP3VtVuwf7EKZUynzHJ0gVIm4YjZu+cQPTup4ZF3c8J2QnLQxt6yK84W6ri6PrYiYiL4ZwFMB3BPAfZn5pozetQCeBmCB/pNADhmdXPlScb5QCbQih9xXUcX2pzi/GcA3Anh1TiGsVnkG+lnJLwbwSCL64iCWlS/XAPid8HdFRcWBQaIm1v25KNiKiJn5rWGJYAn3BXAzM7+DmY8BvAD9iheE/58bfn8ugG/Ypj4VFRX7C97g56LgNHzEdwbwLvX3LQDuF35fd+VLRUXFOcVFGuGui0kiLq1cYeZSXN5gwklb+44Q0fUA5DiETxDRicX07QGuAnDrWVfiBHHI13fI1wYAd98kU90Gs4xJIi6tXJmJWwDcRf39eQDeE36fvfIlLGu8DABEdFNptc15R72+84tDvjagv76zrsMhYtvJujl4LYBriOgLwnru69CveAHGlS/A9MqXioqKc4xug5+Lgq2ImIgeQUS3ALg/gP9CRC8P6Z9LRDcCADOvADwRwMsBvBXAC5n5LcHEDQAeRER/in5lyw3b1KeiomJfwRv9uyjYarKOmV+EeBNlSbcrV24EkOxsn1v5MgOXp1XONer1nV8c8rUBG15f9RGXQZw5gaGioqJiV7hd8/l8z6MnTysavG75hNcdss9dUJc4V1RUnDjO6ISOc4PTmKzbGkT0zUT0FiLqiCj7diSia4no7UR0czij6lyAiD6DiF5JRH8a/v/0jN47ieiPiOgN+z57PXUvqMfTg/xNRPRlZ1HPTTHj+h5IRB8K9+oNRPQjZ1HPTUBEzyai9+VCRDe9d3VlXR7ngoix/VLqfcc6S72/mpnvs8+fazPvxUMAXBN+rkd/GsK5wBrP2u+Ge3UfZv7RU63kdngOgGsL8o3uXZ2sy+NcEPEOllLvOw5tqfece/FwAL/CPf4AwB1CLPl5wHl+1ibBzK8G8JcFlbXvHTuj3ToiHnEuiHgmvKXUdz6juqyLaKk3gNxSbwbwCiJ6XVhpuK+Ycy/O8/2aW/f7E9EbiehlRPQlp1O1U8FG964ScR57M1m3L0upTwo7OuTwAcz8nrAnxyuJ6G1h9LJvmHMv9vp+TWBO3V8P4POZ+aNE9FAAL0b/KX8I2OjeXSRiXRd7Q8QnvJT6zLGLQw5DfDaY+X1E9CL0n8j7SMRz7sVe368JTNadmT+sfr+RiP4dEV3FzIewD8Xa967GEZdxSK6J0lLqfcfkUm8iuh0R3V5+B/BgnOBhhltizr14CYBHhxn4rwDwIXVQ475j8vqI6HOIiMLv90Xf1z5w6jU9GWx07zpa/+eiYG9GxCUQ0SMA/ByAO6JfSv0GZv56UocAMvOKiGQp9QLAs9VS6n3HnEMOPxvAi0LfPgLwfGb+7TOqbxG5e0FEjw/yZ6JfaflQADcD+BiAx51VfdfFzOv7JgBPIKIVgI8DuI7PyeopIvp1AA8EcFXYwuApAK4ANr93dURcRl1ZV1FRceL4G81d+HOvWP8YrXce/0BdWVdRUVGxCzAmDpG94KhEXFFRcSqorok8KhFXVFScCioR53FIURMVFRUV5xKViCsqKk4cDEZL3do/22DbzcLmbsa1C1QirqioOHHIZN26P1ti283C1tmMaytUIq6oqDgVnDYR72CzsFPbjKtO1lVUVJw4GEBLezlZ521gdL/we7QZV9jj5URQibiiouLE0fG7X/6R25581QZZP8kcgnCZmYdz8w5ls7BKxBUVFScOZi5tNL+N3ZPcLGzWZly7QPURV1RUXGSUNnCa3IxrV6hEXFFRcZAgokeETYvuj36zsJeH9M8lohuBfgMnALKB01sBvFBtFnYDgAcR0Z8CeFD4+2TqWjf9qaioqDhb1BFxRUVFxRmjEnFFRUXFGaMScUVFRcUZoxJxRUVFxRmjEnFFRUXFGaMScUVFRcUZoxJxRUVFxRmjEnFFRUXFGeP/Bxiz6UqBY9dvAAAAAElFTkSuQmCC\n", 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\n", 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nt9A4pRaC1qdKqpIL3fItEXnGmMdOBRXiFaH/6T8dvsm6kHX72vl6fZ9vmfhOy9eXqicm0H6bfVHud4xl7vmW/WvRfd73GzZSqUz7FvEgFzoAkHS50KuIaZNjW0eFeMnJC7C9EXwd63Xy/7t2o98BN67w1r3xqvqTw/b47bQRFIXRHe74jgyujwry5JlAGsxYLvRHRvZ7NMkfwmSF/FsRuaDGsVNBhXiJGYhwTIAtIzeH954dgWQEu0FUhBPMKkJb9ebrSHl5sXozQtY4aGvyUL/97mUgyCrGE2T8tJY7BnNZbhWRrfZ1lVzoPwDwRyJyI8l9AHwewO4Vj50aKsRLSP8kl9I5LcBAwkLxbhaxoWvSSzz2wwpcG4ZOkWBLpK3BTS1hmN2Iz5h5Mbbl+nW769bd/L1KTVbqMWZn3dUisimxrTQXuohc770+leQHSO5Y5dhpokK8ZPQ+/mgA3YEQsZvvIUlajUU3ib8tFMBYBwztIJBxXBPjWE4xl4Yrw9smgbU/3ACI8yEL0fv4o7Hhxd+p1walnPZdE4Nc6AB+BZML/QX+DiR3BvAbERGSj4DJwX4NgN+VHTtNVIiXBCPAntiIERYnMOxmRpw6gXKmRK+iGMaF3bWhPR/xiDWba0TaYo8JMgBIz/xY+QLs7+eupwpyi7TcWVcxF/pzALySZA/AHwBsFhEBED221QbWQIV4wRkRYGDUgs0A6dt4tU4HoAyFLSVcEcpcGY0prHt0XVScE8LrBqDIoEPP2+aPTREagbbnqoLcDpNKg1mWC11E3gfgfVWPnRVtTJWkzIhRKzh4rM8wGrvZJ9DrQNa6+aXfMUtEbCXjaB3TnlMsUm+uXWF73fkE54leZ3TYdnidgjqA4bVWGqBTJSVRi3hBWf+3x5qRCt2ILzbmt02I5uDRvG/+sSMQ56vt1jRhWg7YLzQTIp2K6HPgZw4FerSjLmI5u/Z38vtIZspe/7fHYpuXnVnrFBQHNelPASrEC8ba1sfZVy7+F0NXQ8rHGrkBUp124ifV6XsbfHeGfT9xSn5QBm6GGJGoCSAhyCNujOG2sA53/W+15YzS5isBKsRJWnFNkNyL5EUkLyZ5eGT7niSvI3mOXd5U9VhlyFCETSjQYMk6yNY2IFs3i3ssj7kPUo/zKVdDro5ed7isb8gt0vPcG20tkXr8NkjWGbSv8vnEzt/u6+od1LW2IVeHRARZqYgMr32dZVVobBEv03jveSYU4RzuC5u5R+muEZYwhK1Ox1zNYc4yqeQ+IYnoCddeFp1jGMrmXA9+5MSIdcxcnSIc1LG29XFqGSut0IZFPBizLSJrANyY7UkfuzJUEuHBDp4laF9na9a663VN2Ja1cGMkLUy/viZDnpt29JXUX9R+ycxTgrsO7rrErtlInUEdDrWMaxB+/lWWFaENIY6N2d41st+jSf6Q5FdIPqDmsSvLze99IrJbPBH1CUPWStwQ5nG/Y8pb6w7K9N0KUZGr0otd4aYaeexsciMWtcmPmvDPca1rr2Un5wsvclfk6vM3OzG/ZQNufu8T0+1UBuTcaRWXVaGNzrrY1Wp9vDfJLQC2AMDGjRvHbuwi4W5wEQ6HGfc65tGYnsuhSmdcLOS2P/plJwXsDuNow5F5+QKCjrOaVDnGT2GZ7CC0wmv2l+R55St3G/LtKerMk/UuYlP+kIKb3/tE3Oav/6P0fFYWafAktQK0YRFXGu8tIjfa16cC2KbueG8R2Soim0Rk00477dRCs+cbX4RzOGvPWrbZLRuM8Az8wzHrbrT8lMUh/Q6yta6xGnsdZOvd3OLqNlbl5DtVBhZ0n169kXa59tqY6KrnG14b/3xcvYPr3Is/Nbhy1TIuxg3qqLOsCm1YxEsz3nteKBLhHK6zybosBkbehj5SJB/3UmIahG9JWHTRsOQiazrWtoL56ArdFLE2eJnVcsXEOvUk2N7vJB7Lgrn0Ip14ahnHEVToAF5hGgvxMo33ngf+cPSTgU7CT+sT+oN9sVzfMBAKugQ8qVwNZX7fMso6x1pgxK0QaYPZsb4gux8acY/OwX55wWapGAPmM7ztYV8vbvMqoq6JJK0M6FiW8d6z5qZ3Pg1A11hX3WwooiEFIgwgF87mwsoGYVdWOBgm/ykof2TzlC2bUn/vYMdyQR78OGTMhaLl9kuEqw3qCMXYrhcbi0wKbnrn03C7vz2t9NxWhohvXRmiI+vmBCPCwy+r9Ds5Hxk3GOH0RSH6xS4ItTKP3TQ3hdcL5rsQUiJX+yYa1/opyrIWaUu0vYGVnnN5CPIddDFXRcLSHezjlS89r2wO91cxjqBCnESFeA4IRdi8ye+TrRk/MAcWrd2hYDqjws4pX3fWhmFxftTASARBkUi29dgZllNSpz+QJBeql8o9DIyIMRCxfEOXhq3L78gbKdsrV8U4ZLXC0eqiQjxjqojwiMAITaeZJxy0CXqcmIx86VNP86EF7T9qV+iYY8XEQGNb2m6WkDBjGhC3sCL1FM7OEVjH4fWTNd+aHnV9jJStYpxGfcRJVIhnjGQ0N27EShtsz61g7vVAMNwoZicSNh641jRJYfmJbbnH/gqBEaQMhlvHyqxrKRV23iX8xOmkP65Q42YYPDAU+aUDP3FU6G3ZAiY/g5VixcLR6qJCPENu+KdnANgwuJHZyXJZzkrnaRuxekPfqH1ByQl0lAIxHOuRMvRNV6VCVrdKnXclgpz7UUm1L/DHVxVj6dunlqwzWH/DPz0D273hS6Xntqxo+FoxKsQzwogw8oMHssBX65L2dLJKIhwyFHKOGGVO+JPJcmJ1pGjzBqvobsgdUnQONkqClIEwpihLkZkSY1eHP7Am3wYVYwDaWVeACvEMCEXYkbdm/W1GoDvdrFI4W1nZg/K9aAJSBkIzIkgV65wYfl0FbRm6aYahaWU/JixzMUTE2K3PigagJMpeXTFerbSWdVEhnhOSguF35PeGj7rOn9ypMEtzFct2YJlL5IZxbz1/alivX07hRJ8VKCoj63VHO9pS3hZhPJtJsE8omEAYDeGJriTaF/Pvx+KUVxWNIy5EhXjKXPePfwF2iq3V4YbgbRA6BSH6vWAyUAxFpNIXv6jfq8CC6feDTHCh37bm8OZBMaGVWUPIUh1mZmNBnYFrI5dvwm9PxWiJsGy/XMB8B+745lOKT2YZUSFOokI8ZbKe7we2gzQ8C3dAQQibWZGPnsh6cTfD0M8sw+gMIP86rLrKI2TZ6LuSx/bKhPUUCHMyMgIYCmVw/v6PWhZxZ9SKloiJsWBgTbc15HtRUYs4jQrxFLn2jfsNXpvhsFaUvXjdTnf4ZtzoiWg+V68O4wcedgD61mupCM/6Zioaxux2CQRy8KPgOuxqnEOtaAnv2mXhE4PFlXXtG/fDnd56cuV2KMuNCvGUCEV4BHsTZ5n3kdgbvNPxpjqq4u+tYK3mLFYv/8Igx0ULtBofPFJ4cbhdtt5Jjzos8GFX8us6MbZt6K934/VE6vLLXzUxrhJzvqqoEE+ZWvkhbOREvz8MwXKkfLB155pz64a5E0YtuagwdaRSyFtVcQ0t+GTZdToj+wlRjWRZC8tL5fTIzeyRqLcob0Ws/JVAMPunqTlGhXgKXHPEc8ButS9huuMuGKyRmaRAJAbxwH52tSh1IyoiI+mGZRX283lFxAV7pA2he6VC2SMU/DCUZVkbOa7fGYbAede6ap2Vzhnmu3Hnt32mcN9lQCaUa4LkXgDeDRPjeayIHBVsfyGA19u3NwJ4pYj80G67FMANMI67nohsar2BFVEhnjBXve55ADpAbziVzyBxT1WrKBYnLMP/0vfC2qwvmEQ+F0WdARuV3B+luwzKanOG56gYenWZnSqKYxaIgzB3XiO+5pgYBz7jKFn+By2c1eSq1z0PO73j08VlLAFtC3HFWeB/BuDxInItyb0BbAXwSG/7E0Tk6lYbNgYqxFNGvPnVHMYv695UGeI7Wqb/H3AW5TAiwC+3EybqCTujatQ9bVz9pYIcOScnBFmf6X3DoqqIcfS4zmD/JiF5S0X7FvFgFngAIOlmgR8IsYh829v/uzDTsc0dKsQTxFjDAbEIBz+kLeEHdjd/SoSTeCPBHP314IcgFIbY6Lo5I/WDMJgdOrdvid+6gRiL5/ss8h8XJpfHCljFUuG7Wp/YLPCPTOwLAC8D8JV8q/A1mg/nQyKyte0GVkWFeJqU5YtA0GnlxZ12PD+wE+hxQs1SdYbHSRYXk6LE9IWzfoxBGHcb1t2kY7CJGPux4LHrmXIDlYnxsjOma2JHkmd577d6ghkrMHpRST4BRogf661+jIhcTvIuAE4neaGInDFOI5uiQjxnpL6sLi5VAKA/Oihk5H1FEa7Thpz4xcqf8IAFAYrFMtUxl9i3SIz9c6maoa2wbGXMHlhcXdCJVmkWeJIPAnAsgL1F5JpBc0Qut/+vJHkyjKtjJkLcyp1Dci+SF5G8mOThke0vJHmuXb5N8sHetktJnkfynOCXb6G54tUvRH9tm8EiWQeZN5igaceF6/DJet3cMqhzfQMyW6dbKpVb1q4iUZoGFeqvem3D69Nf3zC4fv41dZ2hTR+t/c8+yzqQrJP7jlzx6hc2Kn++GQ4yqrOUMJhBnuStYGaBz40dJ7kRwOcAvFhEfuKt35bkdu41gKcCOL/FE65FY4t4mXou28LdUL4fs7dmLnVoKXW6Uv6IGro0CgQh1+vfS4uvCXuL1DtGvuIiqopXbZ90yQi7mHWexWb5SLUnFVAyMqw57mIIozHCulPW+xWvfiHu9p5PVm7noiBoP2qi4gzybwJwZwAfoPlQXZjaXQGcbNdtAHCCiHy11QbWoA3XxNL0XE6D8MuY6zjzIxs69TvMkh1YqfC3LC8W4GjMrwjRqdCO5HDsioTHVS+PyLJRV8BAiCu4CBq7EbywtCy8poNKVtxVMZnOOlSYQf5gAAdHjrsEwIPD9bOiDSFemp7LSVE5Xtd7dO17rl8/eQ+ASsJYWnesHYnHwZQlmRsJ13JH3SD2NssnM0pReI0rDK4Y/QFKW8V9/0mjaqdhgeW8Mv5kHVmXpA0hnkrPJcktALYAwMaNG5u3ekJMws/nOoucReFLXpgX2BfEWiJctz3huilkFqvTGZc/sL4YS9bJfYljSeAnFeK3tO4JFeIkbQjxVHouraW8FQA2bdo0tyZEf22bwevOBj/lWaSHPkIyYiHxWJf1O8EjsEtgPvShdrpZ3np1FnbNG2NebqSxBNmJsYzmtMhdQ1dmBbdCKoF90sqN/SB47ckieT6Wh8kMcV4W2hDiQc8lgF/B9Fy+wN+hqOcSQEdEbvB6Lt/SQptmhv9l669viI4E82OB/XXtNGDU3dFfH/2YY/WNm8w92ZSaPsH6PvGS6ImIFVsa99t6h1LQSdfr5tZVHam38Ei6D0NpQYiXqeeyKb98xUHJbbloBiFgJwodDNIIhLpy586YoVxZTCSzVA7dak0BmvmKc0O0a7g66tzgsXMp9dMWuDZiuaJDoS0rPybGv3zFQbjHhz6SbtOCMYmoiWWilQEdy9JzOStMzKS/Jh+zyiDlJFl90EJ8fZO2lZAQ80lTfVRdAzHG6PUQGf2sFKUuOrJugpQJWOWRbpmXwSy0zpy/NJLQPS44laqs1b55oE70QUqMw6cEEeZm9xhuKO/8q9u+lXBR6CzOSVSIW+JnB70c7HRGLKJxfL9j5ZDod6qNIC1I6FOUR2IRKErontsvkhiovPDqP5rjWMUjlna/g58d9HLc8yMfrl3WvLKI36lpoULcEsZ66ubmhgNGhZidbGSwQtuPs2XJ3t2UNeMkzWk9XnhMqviQx00K1HZc7+CHtTN8H7Z/6UVKVuAcG6BCPGFGcjxEBGTUpSCFyXsmRWkUgjDa/lkx04EQ4bXykgWlhjMXsfyDOjR8rQgV4lkTsRQEBEaSiSdCzuyw5Lpf8knvPw3qjkqrun8YZ5xKQGMG2lSsfP4u39SZx+/QvKBC3AKXHLhlIuWGvuI6Uw6xI8mbv671Nc830DhinB4OXT/22WcSEROXHLgFf3z8koz6n+Pv0axRIW6BYY5eJC3XqdTvr6uRaSxGp5sNhbzu6TS93+qOYMbw+seGIjdlJp+n/S4tCyIY9E0oo6gQt4BLcelws2mE6+YlxrSKhduvMdw2fJRvKlyh1dq2EM7EHxsb8ZxxpA+BlMq5oxeNeX6ymjUqxA25aP9XjazLBhNHcmRdSJFwdeYkQiFFym/aft7ZMZP9TICUK0SEyBL5n6tek5Sv/6L9X4X7nvj++o2dM1SI06gQz5ii6YeyftoqLQsjSwl8WzfDLG6qtlJGFolpaeRIHWt1Dn445geNmihChXheqBvF4E27FKOKCOfC5Er2rVL+NCh7bE+1K3a96v5QTSL95iqhQpxGhXhJqfKlFyGy9fFyQ3BGbsxxO3xi7VVhmCICjZooQIW4Ib21DYWWEjvWjTCHX8ImvdiL1gMu2Yx+PMo+d0rStzwoYg6/O3XR7GvFqBA3pMivKEI75LnY6iQlGmkR229WlqiSFxLJqrlvshIXUrj/MqNCnEaFeELU+dKJEP2Cjrkc/fJdXJmdzmhGtirH+cx75IYjFgYWIgXXTsSEks1SDJd6/jqNIy5EhbgB5z/7sKnWV9eiGCseNYzcWJSY1kTuh9rF1LjG0xTN8599GB74uaOnVl/7aNREESrEC8LMvsR68yRZagtWmSoqxA3o9/KXrygcLEaroVLKXND0M3XHl4UnLiLLdj5tokLcgHheg3amCupMeCJPk0ui2szSC0kshlc48pk1HXYeHj2JXBfLgEZNFNOKEJPcC8C7YVToWBE5KthOu30fADcBOFBEflDl2EWl6Yy1/ZKQpqZkM5pbbu6Ys+l7lnm6pEkI8bJoT+O7nWQXwPsB7A1gDwD7k9wj2G1vALvbZQuAD9Y4VlGURUeGoZ51liKWSXvaMLseAeBiEblERNYAnARg32CffQF8TAzfBbA9ybtVPFZRlIWnvghXsKCXRnvaEOJdAfzSe3+ZXVdlnyrHzi0i6WXZGOcmmsaybCz1dypj/aWYudIekt8guU+wrlJW/zZ8xLGrFX5tUvtUOdYUQG6BebTAxo0b67RvYqyvbzPrJkyFeY/gWEZBXkbG/Jx2JHmW936riDhxm4r21OCeAF5P8k9F5B/tuk1VDmxDiC8DcA/v/d0BXF5xn1tVOBYAYC/+VgDYtGnTXChD0bQ685IEvg1U6KZHk6ma5hkZfxbnq0UkJWZT0Z4a/A7AkwC8h+QXAbyo6oFtuCa+D2B3kvckeSsAmwGcEuxzCoADaHgUgOtE5IqKxyqKsgQUuV3GdMfMm/ZQRHoi8lcAPgvgTAB3qXJgY4tYRHokDwVwGkwYyHEicgHJQ+z2YwCcChM+cjFMCMlBRcc2bZOiKPNH+zO3zJ32HOO17XiS5wEYncInQitxxCJyKswJ++v8RkmqQbFjl4FxHzGdS6POnHGlZUZ8vAuTQ6JlYkmM2hSI7gaTWWhZXQzjM5nO1XnSHhH5UPD+bAAvrXKsjqxrQChmLoNXFco6wKrMJjEJ5r1jriqpm75ylrsx6yn7/KuK0TiZ8+Yd7WtIo0LcgF6B1Vo2Qkq/lMtJ08/V+UX7SzZUukFn3UqgQrwgzCrT16LcPHpt5h9116RRIW7A4771Zpzx5/9YvmNL1J3pIcuK/XJx8Qpnkl4My6xKkqSya9Gx/vl5FNjHfevNs25CY+bxus4LKsRzQNXHNikR1roskw+yyg9G2bWrOPkJAJfytPz6kVI5kc9SjJ5LspwjIdtChbghIkw+crEjkMxMgzQJ0VsmIV0EcnPWCYEWMpWKEN1uf/BdibFMg4OUOCrEDVlfSw9z9oWyykST06ap33naFs4itbVOvf1+F+gv+aO7dtYVokK8pMS+9KGQFU1aWje8blqM265YOFiVa6S0gyaGL0aFeE6oa51WiVdtIjTzaMED4w9ECX9wUtem7LzqzGqtwpNHr0caFeIZUySYWWLeMlJy6+uIYlthcLMIp2vrRk6Vk/os/HVFn0mnk40t8KuACnEaFeKGPPl7R+Drj3hbbp37wo3cwDXjKFOdNPPyhXbtCH3hTdsXCte8nC9QLOK99Xq3EzsStbBD0X7y946oVe58olETRagQt4DrsHOWah1rdV4HImT9zthWbxtCPE6dpLQ+6WpbRC3t/nAy0/A7Ez71LDyiAzqKUCFugZgFPIv6c+sKYo7rtHMRXBD+9a/iQ06dT9XY4EmzjLOPaGddMSrELbDPD1+LUx/8L62X61sQKUs7yzrIhOjUFMtZ+JUnwTiiXXhMMKrDXVvnQohZrpNknx++dqLlT5PlHrDSDBXiGRMTBhGiby27qjd65pVRRZTrius8ivG4lnMZWbBfJkRWkrXNld2NhMnN23WbFeF1VYaoEE+Y8FG5n3Ugks/OFloK+W3TS4dZJlT+D8Ss6VYII5vFtXKfb74d/nHmfdj+pRdrHdBRiApxS4jQWE5Bh0Sqg0IwuaGr7nE6ZoEYCzx+nN/WefCVFtFL5Jaocg5kXPhS16wp7nq7tgkwYmGHbe10pLa7aZ4RjZooRIW4JZ5+7t/g5Pu9J7duXCsntJhHt8d8xfmcF33ELZDKAzoyzr0Yh4Q/ekU/giKjQj7iUujEM7IVXcNx/aBh52rWB/a78NXjFTanqBCnUSGeIGV+1apiJ7nQH1buVY/VXcfXu0hiXCc0KnXtRiIustGMbGEHHTvVsquVtW8VRGoVznFcGgkxyR0AfArAbgAuBfA8Ebk22OceAD4GYGeYfFVbReTddtuRAF4O4Cq7+9/beaRWitAaqhO+VGydxUW3rhjXuYHamguvzlDiup2OddaH20f2C5R6RKjnJCRu5mgccSFNLeLDAXxDRI4iebh9//pgnx6A14rID0huB+BskqeLyI/s9neJyDsbtmMu2O/CV4+4Jxyh0IadXrFRaj4pf2Gp1Z3YHhPMtjvi6vo4s9yjefO55WIdeqkfpiKKto/4lAPBDusLoypSAzeWzS0BqEVcRFMh3hfAnvb1RwF8E4EQi8gVAK6wr28g+WMAuwL4EZYQ/8vWs2Li8kb4TKqXPBQAEaIX5EN2FnfdNtTdf9LhSmU3di+S6Cc2fNoXx7IfxKZtXO/lbzk/P8WGbp3U9IuFdtYV01SI72qFFiJyBcm7FO1McjcADwXw397qQ0keAOAsGMv52tixi8La+mh+4jrugaQFmxi0EVqxvuCX5WsYJ5Y4LHcWjHND+66F8Hgn2P760DUSs65TPzR13B9+EqG1OQkNVKZPqRCT/DqMfzfkDXUqInl7AJ8FcJiIXG9XfxDAW2E6st8K4F8AvDRx/BYAWwBg48aNdaqeKpsvOQQn/fExrZbpxMNJQb8fv9E7wX1cRWjHsYxjgtyWbzjEF8RxLaoqx4X7ZFkHmae9PQzPr9sNfMAt/zBtvuSQVsubF9QiTlMqxCLy5NQ2kr8heTdrDd8NwJWJ/baBEeFPisjnvLJ/4+3zYQBfKmjHVgBbAWDTpk0L1ftR1fr1rbZ+P/0o1/aIOH+fmAslJfxhGXXbVq288rq73VE/a2xIclldPllBX2GvF//RIWVEpIvqqNq+ZUFH1qVp6po4BcBLABxl/38h3IHmTvo3AD8WkX8Ntt3NuTYA7Afg/IbtmXtGHot7o9m3QioPc85GreKismMC21T8xx0J2CSaISaaMQH3RbJN8kmHwnZItO5Zu3emzgxG1i1SVFfT58mjADyF5E8BPMW+B8ldSLoGPwbAiwE8keQ5dtnHbns7yfNIngvgCQBe07A9c4F7tHQ3ZZYB6+vdwXLL2obBa/MI3CkMWYvlokiRZcOl1+vkFr8Nft3+kmLcjGDuuLJl3HJTxM4tPP/w+vjXrqjeovdh+8K6/c/efAauveb/0rolUP270OR7EeCiunYH8A37PsRFdd0fwKMAvIrkHt72d4nIQ+wysdDaRhaxiFwD4EmR9ZcD2Me+PhNA9IqKyIub1D/PbL7kEBy/67G5db6RNk7GNJ+w88mFQfUzJgcYtJEMaNYdduO6GmKEHZ3uvQjQ7QxzArflB3aP5pkQEDvU3BuqfeCvDm5cxzwj008VvTBRXTqybs6oEvfr8izk9/PjlO2aQI+qin9KbEVYmOWt33LAfjcYCBHWXWUy0BTxPBzD1+ZchqMZ/fI3eMnnx4lLXk1mEr62MFFdKsRTJMwhERNGZy0ByCUQioljufVaTYxDUZKK/rws/qDTGr2CTsJU3cZ6za8rO18gL8Ix8vHhnejn0fF+OKpc45VCxu6s25HkWd77rbbjHsD8RHU1RYV4ghz4q4NH3BMxMc68YcSxL2unQHyrirF/468HAx3q5DKel06mMn960Tn5IXHhtalSl//ef0roe4/ers5OJxv5YYjVt/RuCYz9pHC1iGxKljsnUV1NUSGeMmakWwedRFgYK3SfpsQ4JRBA3D8Xq6vMfbEIPuJYYndgeA363g+Rfw1Cd0ds+HFl/7P9bP26ul2TZjOWPH4VmIFrYmGiulSIJ8yBvzoYx+78EWSB6PqP1mU3ZZE4uphjdozQ1AmDkywtxkCxdVz1psqEpZ00bYWVFT36xtogQog3qjj8TNw17XbTk3lWedx2x/V61lr3xLnTFRz864NKy1gGZiDERwH4NMmXAfgFgOcCJqoLwLEisg+GUV3nkTzHHufC1N5O8iEwBv2lAF4xqYaqEE+Bg399EI7Z8aMAyh/9i8TRF5N+FiTFsdvKxDMmxkCxIKdErMyN4pdRRD+rdlysgw4ofopI/QiUiUI/6wyuqe9y8Ic6p+qtEh3gzm/Lrw8s33kpiH9fJskiRXWpEE+ZmHU74mrIhuuBYTRCyh9cJywuKsbWMiQlOtVPHfriW5XVj8tcOFdp2ZFCR1JRGsEsiv4oa0t+/+FrF7HiX0cX3VE1gmIVR5iZDuBZt2J+USGeEodc/ZKBVZwSYyA/BVBmd+kwv18TVwZgrL0sG5bjh511SjSi3vDqyrvWpkhMRfKZ17reLBtlUxDVcTW4/2s958oZ7uOHuKXKP+Tql5TWtUxoPuI0KsRTJBRjF56WRcLFcgl1pFiMY5ORZmAuDC0LtMeUMdrGsK6Qxeisy7/3nyjMttEfHhf2Vjaxa1i/X5f/OTrXESmDOvzwtlUTYUDjq4tQIZ4yvf5oUviYhRaKbZkY9/pEh2Y/V36pPzo1eCRiiceO9QnLGXdwRziIo94gjfS20Ip19AfHDH2Y3U42uJZ+e4pEONVWEWLdXQvrQqkyA/WyIVhNl0xVVIinzKHXHoCj7/AJAEPLK+VKKBJKsf7S0Lca+ouBatEPRYIc2yc1i3KVuNwiev3iMvxH/irW+Tjhbv2sg54M2wPk/cBOpKvUkwshtMcceu0BpW1aOtRHXIgK8ZxQJMaOtX7cqgvdFGZdedmpekJrNiYyKfFr42YrKmO9NzqF0uh0RNUbUWXEm4n9Hg03LKtHLcA86ppIo0I8Aw67/kU4+g6fiI6y83E+5H5/NALBT3cZj4QoLjscxhzLOBZLqenX6Zi0v7jsBh74wQcDNjwfsHcOseHP+XLK6zZ1uM/FPI10u66u4hGOgPnsFSVEhXhGhGIsMryxYy6FEWHNqomx25Zl+XUh0Y47F5tcMtqvjsVcxjhWU1Hayn4utG00tK7TKbLuYyLsb8/Xsd4b+uZJNxBERdgw/TjiRUKFeIYcdv2L8PbbnpATYyDtO64ixrnpffrVU2KmMra5enLHVhyG7R9f9ANQpbyi9sTrTxzr2uVE1Fq4IsAGb4Rf2KaUCI+Ui6Frp59x8Jn93R9eUN7oJUagPuIiVIhnjBsA0Ul8S4vE2FjRthwXCmetvg5ksA8Qz8IG1BPk0AL06y0jlS2tAxkZkBEj5o8tauNI/QlrLD9Yg8Pr1w8Ha6TrKCo7Aweiv+qojziNCvGMOeLm/fG225yIDMyJZ6qzzaTJHAp4Umi98gr3C4Tetxj7Eb9yijqj6MJ2FjEU/5j7I/++66ekjJ1TpNxUWzLPD+zX5erIfR6Jcl0ZR9y8/2hlq4akf7AUFeK5oIoYrw2G1g7Xxay0nIAHYuyX65fjT+Nex4cclltn/6rllO0bEz5T//C1L9Cp0LjwB2F0IIdn4Xrlb+iM/tj55akID5nBDB0LgwrxnBCKcd8m+SlKrBMT3xFr2nNVuNF2kGKXQjyMbvi6ishOdnhz8Xb31ODoeyLrXA1OnGMWeUyEU+W76zjooKOoCEcwPmK1iFM0mjyU5A4kTyf5U/v/Ton9LrWThJ7jZ9uvevyqcMTN+6PXJ27pd9DLaMQYoy7GstjXYbSE6WRyZa73iV5mlli5fvmFKSUlv0yaqvUVtdudrzv/W/odc537zHUmVhLhSLl9GZbb61NFeAQOPp86y6rQdBbnKrOkOp5gZ0L1s+3XOX4leOP6ZmQSGbkV7BePCzZLLyNu6RFrfTO8tm/Liw0BbiLIbp9+Fl/W+vWWVDlV2lAmwKPH2OHgAqzbtt7Ssz9SCdGPiXCszDeuby5s70oioz+oVZZVoakQ7wszOyrs/2dN+fil5MieuZGriHFmcxmsZcTNffO/L0MBSolFSEqwnLXnlrXM1OeWniC5+G2oshSV5de5ljHXplS7iwQ4tq85V+Su5XoWt85i1xUYfnZKHgGSVq9axM2FODdLKoDULKkC4Gskzya5ZYzjV44iMe5bcbolI27xLF63f5mAp/YDnBANBdCIEbCeYSCAPbsUWdpt4Vuuw3o50q5he/1EPuXnW/RD5ep117ln36sIj4daxGlKO+tamiX1MSJyuZ3O+nSSF4rIGTWOhxXwLQCwcePGOocuLEf2NuPIDScNBAHIh5S57GgCIrQLR7K12f++jdH3yhU/dEvyYtZJ9M7ljym/a8JsblUEXArC29w2I5701mNwwv516UauR1l7/PoHWdRgOuU6HF5PFeFytLMuTakQtzFLqp2aBCJyJcmTATwCwBkAKh1vj90KYCsAbNq0aWV+K4/sbcYRnU8N3vtfZV9snWD4whOms3Qi3hdTTuwixm6VLCfK8XYWCeawnHyNVY5Jl1W8bbjZG/Fm1ztBHkROlAhwbJ+eFznxtuz5ldu9ykzyyWnRaeqacLOkAulZUrcluZ17DeCpGM6GWnq8kr/RU4/Fw+0ciIh7vWb9qusCrAuQIT2YLeVb9esb94Zy7fHbWJey+ova34c5d3cd3HWJXbOwzrAOh4pwNcZxS6ySa6KpEB8F4CkkfwrgKfY9SO5C8lS7z10BnEnyhwC+B+DLIvLVouOVUaqIsROpdQHWhFjLhj7TfuSLnXlLSFVBzkUftLyEdYwjwKlzFBleF3ed1oS5ev1r69fjUBGuh3bWpWk0oKPiLKmXAHhwneOVOG/Lnj9wU/iCkAHoZXFBBfK/tukh0aP7+vVkyAv5NAZJxax2v31k2pJItS8+qs5tzNfbkXQdKsL1WSULty46sm7BcALwd/yUEcfIPqEdkSEisAlB7stw/wq5eArrbUrs3PoFO3QxbHt4XslkQAX19r03tOW+XVSAx0WFOE1T14QyI94uz0cPQ7+nLyixx/SUC2LweG6XdQA9AGtB2anjfaTlpYiwXX3b5h7MObjz8SNDyq5HWK9fdg8qwsrkUCFeYI62wuC7D8oEuQ8rVMHSt0u4fyiMdYS5TWL1pkRbMDyf8DzXMWrppwQY3vqjVYQbIVAfcRHqmlhwnEAcRuM7JvKuiFBQQhGKuTGA+C90KHiurhR1f+WrWNzjlhGet3NjuBnwUtdLBbg9pu2ZILkDgE8B2A3ApQCeJyLXRva7FMANsA8/Lg1D1ePbQC3iJeFoeT6OlucPrLt1OMtXsAbBul1C6kYbxI4NF/cov15zca6Wuq6KcaI/1iHo22tjro9pg9vfXU+lJRJRMGVLQxYmF44K8ZJxtDwft3jCG4pTBkFWQ5DNMXl/qb+sQ3BLsKxD0PPaUHVxx8TKC+st+6FI/8CMnn9mz8PVrQLcPmEMedWlIftiQXLhqGtiCfmgmOG2h/AkAHF3gxOjTpgQPVJeDzLiQw3LS1H1Vioyfoz4SrReAthQZVRfpIbQDXGM6DDlSTKDkXW5XDY2xUIMgcmFIwA+ZEfx1jm+MSrES8wxshmH8KRcRxsQF2R4+4j3H0iLaZlPt4Niga1bXmq/nq3FhZi5/2XH+21TEZ48Y+rwjn4OcwBbPaGcm1w4TVEhXnKO8azjUJCBYSdW37tNyqzkqlbupKIqUje080/7Py5d29pusJ9DBXg6mKiJsQ69OvDb5sudk1w4TVEhXhF8wXk5T8oJ72jkRFqUgbQQTmNAR4qY6wEwPzAC0xEIGGH+sIrvTJi+Z2KQy+YoFOTCAdARkRu8XDhvqXp8W2hn3QoSClFRZILr3Ep18qXKaWMpoqxdqXJUhGfHDKImFiYXjlrEK8pxMpxT7aU8cfC63C9c/e5wvuYqON9uU2Kt889VmR3TtogXKReOCrESFeWym8Z3bYRhZOM8ZvkDLsKy/PK6FeVaxXe+qPOjvIqoECs5fAE7kCdCbNhYBkAoYIVhp23ecKHIuwgJ1xYn1ARxvIrvXDMDH/HCoEKsJAmF7QWdT0IYv52qCPQ4pOoDgE/KCyZSpzIZ1CJOo0KsVOaE7IWD1y/ofDK3rUgwJ9UGZXEQQNNgFqBCrIxFkSCGIt1m2criohZxGhVipXVUSJUYahCn0ThiRVGUGdNIiEnuQPJ0kj+1/+8U2ee+JM/xlutJHma3HUnyV962fZq0R1GU+cSFr9VdVoWmFnFpvk4Rucjm+XwIgIcDuAnAyd4u73LbReTU8HhFUZYDFeI0TYW4br7OJwH4XxH5ecN6FUVZMNoe5r5MNBXiXL5OAGX5OjcDODFYdyjJc0keF3NtKIqy+KhrophSISb5dZLnR5Z961RE8lYA/gLAv3urPwjgXgAeAuAKAP9ScPwWkmeRPOuqq66qU7WiKDNHxvpbFUrD19rI92nZG8APROQ3XtmD1yQ/DOBLBe3YCmArAGzatGl1PiFFWRJWycKtS1PXhMvXCZTn69wfgVvCirdjPwDnN2yPoihzivqI0zQV4ir5PkHydnb754Lj307yPJLnAngCgNc0bI+iKHOI+oiLaTSyrkq+T/v+JgB3juz34ib1K4qyOIyVj2RFzGId4qwoylRYJQu3LirEiqJMHE0MX4wKsaIoU2GVwtHqokKsKMpUUIs4jQqxoigTx4SjqUWcQoVYUZSpoBZxGhViRVGmwljTGq6IEa2J4RVFUWaMWsSKokwcE762IubtGKhFrCjKVJj2EOdFmkFIhVhRlCkwkzSYCzODkAqxoigTZ0ZJfxZmBiEVYkVRpkIGqb00ZGFmEFIhVhRlKgjrLwB2dDPz2GWLX+a8zCDUFI2aUBRl4jSImrhaRDYly52TGYSaohaxoihTYQaddQszg5AKsaIoU2EGnXULM4OQuiYURZk40k7nW706F2gGIRViRVGmgo6rS9PINUHyuSQvIJmRTDrUSe5F8iKSF5M83FtfOvJFUZTlIKPUXlaFpj7i8wE8G8AZqR1IdgG8H6ZXcg8A+5Pcw24uHfmiKMri46ImphxHvDA0EmIR+bGIXFSy2yMAXCwil4jIGoCTYEa8APVHviiKsqDIGMuqMA0f8a4Afum9vwzAI+3r3MgXkmUjXxRFWVBWycKtS6kQk/w6gJ0jm94gIkVxeYMiIutqfyJ2RI0bVXMLyYnF9M0BOwK4etaNmCDLfH7LfG4AcN9xDtI0mMWUCnHRyJWKXAbgHt77uwO43L6uPPJFRLYC2AoAJM8qGm2z6Oj5LS7LfG6AOb9Zt2EZmcaAju8D2J3kPe147s0wI16AeiNfFEVZYGYwoGNhaBq+th/JywA8GsCXSZ5m1w9GrohID8ChAE4D8GMAnxaRC2wR0ZEviqIsGzPJR7wwNOqsE5GTkU+i7NaHI1dOBTCSVDk18qUCW8c4ZpHQ81tclvncgDHPT33ExVBEL46iKJNl284fyf03HFH7uLPXX3n2MvvcHTrEWVGUieNm6FDiLET2taZDqeedqkO9SV5qs0GdM++912WfBQ3vsdvPJfmwWbRzXCqc354kr/MmnnzTLNo5DnY2iitTIaLjfnY6si7NQggxmg+lnnfqDPV+gp3IcG4f1yp+FnsD2N0uW2BmQ1gIanzXvuVNPPmWqTayGccD2Ktg+1ifnXbWpVkIIW5hKPW8s2xDvat8FvsC+JgYvgtg+yAR9zyzyN+1UkTkDAC/Ldil9mcnEWtXLeIhCyHEFYkNpd51Rm2pS9VJDgXA10ieHc7dNWdU+SwW+fOq2vZHk/whya+QfMB0mjYVxvrsVIjTzE1n3bwMpZ4URedXo5jHiMjlNifH6SQvtNbLvFHls5jrz6uEKm3/AYA/EpEbSe4D4PMwj/LLwFif3SoJa13mRognPJR65rQxyaGNz4aIXEnyZJhH5HkU4iqfxVx/XiWUtl1Ervden0ryAyR3FJFlyENR+7PTOOJilsk1UTSUet4pHepNcluS27nXAJ6KCU5m2JAqn8UpAA6wPfCPAnCdc88sAKXnR3JnkrSvHwFzr10z9ZZOhrE+u4z1l1VhbiziIkjuB+C9AHaCGUp9jog8jeQuAI4VkX1EpEfSDaXuAjjOG0o97xwF4NMkXwbgFwCeC5ih4rDnB+CuAE629/YGACeIyFdn1N5CUp8FyUPs9mNgRlruA+BiADcBOGhW7a1LxfN7DoBXkuwB+AOAzbIgo6dInghgTwA72hQGbwawDTD+Z6cWcTE6sk5RlIlz6849ZJdtXlv7uEvXXqMj6xRFUdpAAPTVIk6iQqwoylRQ10QaFWJFUaaCCnGaZYqaUBRFWUhUiBVFmTgCQZ9Z7aUJTZOFVU3G1QYqxIqiTBzXWVd3aUjTZGF1knE1QoVYUZSpMG0hbiFZ2NSScWlnnaIoE0cA9DmXnXWxBEaPtK9zybhsjpeJoEKsKMrEyeRXp91w8xE7jnHobYJJELaKyGDevGVJFqZCrCjKxBGRokTzTcqdZLKwSsm42kB9xIqirDJFCZxKk3G1hQqxoihLCcn9bNKiR8MkCzvNrt+F5KmASeAEwCVw+jGAT3vJwo4C8BSSPwXwFPt+Mm3VpD+KoiizRS1iRVGUGaNCrCiKMmNUiBVFUWaMCrGiKMqMUSFWFEWZMSrEiqIoM0aFWFEUZcaoECuKosyY/w+9WGwHGU0EqwAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "normal = np.array([0.0, 1.0, 0.0])\n", + "\n", + "yvals = np.linspace(0, 0.95, 5)\n", + "for yval in yvals:\n", + " center = np.array([0.0, yval, 0.0])\n", + " slc = ds.cartesian_cutting(normal, center)\n", + " frb = slc.to_frb(2.0, 400)\n", + "\n", + " vals = frb[\"index\", \"z_val\"]\n", + " vals[~frb.get_mask((\"index\", \"z_val\"))] = np.nan\n", + "\n", + " fig = plt.figure(figsize=(5, 5))\n", + " plt.imshow(\n", + " vals.to(\"code_length\"),\n", + " extent=frb.bounds,\n", + " origin=\"lower\",\n", + " cmap=\"plasma\",\n", + " clim=(-1, 1),\n", + " )\n", + " plt.title(f\"yval = {yval}\")\n", + " c = plt.colorbar()\n", + " c.set_label(\"z\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "384b3ba6-13c5-4a64-9751-598b96c47927", + "metadata": {}, + "source": [ + "### cross sections\n", + "\n", + "Like other yt data objects, the `YTCartesianCuttingPlane` accepts a `data_source` argument. This allows you to easily create a cross section by limiting the cutting plane by a region object. \n", + "\n", + "First, create a region that samples a smaller region of the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cf875bad-2abd-4701-a19d-0b713fca0876", + "metadata": {}, + "outputs": [], + "source": [ + "left_edge = ds.arr([0.5, 30 * np.pi / 180, 20 * np.pi / 180], \"code_length\")\n", + "right_edge = ds.arr([0.9, 60 * np.pi / 180, 40 * np.pi / 180], \"code_length\")\n", + "c = (left_edge + right_edge) / 2.0\n", + "\n", + "region = ds.region(c, left_edge, right_edge)" + ] + }, + { + "cell_type": "markdown", + "id": "37ee8cab-d3be-433e-989d-19109a7581de", + "metadata": {}, + "source": [ + "now, define a start and end point. We'll construct a plane by taking the vectors defined by distance between the origin and each point and calculating a normal. In this case, we'll fix $\\phi$ and $r$ and vary $\\theta$ only:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "416f3530-adb0-44e6-a07d-4ac8d5e66e07", + "metadata": {}, + "outputs": [], + "source": [ + "pt1 = np.array([0.9, left_edge[1].d, c[2].d])\n", + "pt2 = np.array([0.9, right_edge[1].d, c[2].d])" + ] + }, + { + "cell_type": "markdown", + "id": "eb8b9441-ff91-4bf6-8185-d9a185601aee", + "metadata": {}, + "source": [ + "now, let's convert all our points to cartesian, calculate the normal vector and set the center point. Additionally, we'll provide a north vector pointing towards the center so that our cross-section image is oriented \"up\": " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "050bd337-7657-4dcc-bc01-ddbe216fec36", + "metadata": {}, + "outputs": [], + "source": [ + "pts = np.column_stack([pt1, pt2, c.d])\n", + "x, y, z = spherical_to_cartesian(pts[0, :], pts[1, :], pts[2, :])\n", + "\n", + "normal = -np.cross((x[0], y[0], z[0]), (x[1], y[1], z[1]))\n", + "center = np.array([x[2], y[2], z[2]])\n", + "north_vector = center" + ] + }, + { + "cell_type": "markdown", + "id": "c52678ff-5fa5-4fc6-9f21-bc1ea663d398", + "metadata": {}, + "source": [ + "now contstruct the plane and create an FRB and we'll have our cross section!" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ee9a6e49-c7c1-4bc1-8c53-24adefcc58b1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:32,848 Making a fixed resolution buffer of (dim_theta) 400 by 400\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "slc = ds.cartesian_cutting(\n", + " normal, center, north_vector=north_vector, data_source=region\n", + ")\n", + "frb = slc.to_frb(0.5, 400)\n", + "\n", + "vals = frb[\"dim_theta\"]\n", + "vals[~frb.get_mask(\"dim_theta\")] = np.nan\n", + "\n", + "fig, axs = plt.subplots(1)\n", + "im = axs.imshow(\n", + " vals,\n", + " extent=frb.bounds,\n", + " origin=\"lower\",\n", + " cmap=\"plasma\",\n", + ")\n", + "axs.axes.axes.set_axis_off()\n", + "c = plt.colorbar(im)\n", + "c.set_label(r\"$\\theta$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "44a1b806-357c-4604-9209-a6326c9c9e40", + "metadata": {}, + "source": [ + "### slices through a limited domain\n", + "\n", + "The slicing works just as well for datasets that do not span the full spherical domain" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "904a296b-f28f-4d5f-be59-7d1569908786", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:34,994 Parameters: current_time = 0.0\n", + "yt : [INFO ] 2024-03-28 15:23:34,994 Parameters: domain_dimensions = [32 32 32]\n", + "yt : [INFO ] 2024-03-28 15:23:34,994 Parameters: domain_left_edge = [0.5 0.52359878 0.34906585]\n", + "yt : [INFO ] 2024-03-28 15:23:34,995 Parameters: domain_right_edge = [1. 1.04719755 0.6981317 ]\n", + "yt : [INFO ] 2024-03-28 15:23:34,996 Parameters: cosmological_simulation = 0\n", + "yt : [INFO ] 2024-03-28 15:23:35,071 Making a fixed resolution buffer of (dim_theta) 600 by 600\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bbox = np.array([[0.5, 1.0], [30, 60], [20, 40]])\n", + "bbox[1:, :] = bbox[1:, :] * np.pi / 180 # convert angle ranges to radians\n", + "\n", + "\n", + "shp = (32, 32, 32)\n", + "data = {\"density\": np.random.random(shp)}\n", + "\n", + "ds = yt.load_uniform_grid(\n", + " data,\n", + " shp,\n", + " bbox=bbox,\n", + " geometry=\"spherical\",\n", + " axis_order=(\"r\", \"theta\", \"phi\"),\n", + " length_unit=\"m\",\n", + ")\n", + "\n", + "for fld in (\"theta\", \"phi\", \"r\"):\n", + " ds.add_field(\n", + " name=(\"stream\", f\"dim_{fld}\"),\n", + " function=_get_slice_func(f\"dim_{fld}\"),\n", + " sampling_type=\"cell\",\n", + " units=\"dimensionless\",\n", + " take_log=False,\n", + " )\n", + "\n", + "phi_val = ds.domain_center.d[2]\n", + "pt1 = np.array([1.0, ds.domain_left_edge[1].d, phi_val])\n", + "pt2 = np.array([1.0, ds.domain_right_edge[1].d, phi_val])\n", + "c = np.array([0.75, ds.domain_center[1].d, phi_val])\n", + "\n", + "pts = np.column_stack([pt1, pt2, c])\n", + "x, y, z = spherical_to_cartesian(pts[0, :], pts[1, :], pts[2, :])\n", + "normal = -np.cross((x[0], y[0], z[0]), (x[1], y[1], z[1]))\n", + "center = np.array([x[2], y[2], z[2]])\n", + "north_vector = center\n", + "\n", + "slc = ds.cartesian_cutting(normal, center, north_vector=north_vector)\n", + "frb = slc.to_frb(0.6, 600)\n", + "vals = frb[\"dim_theta\"]\n", + "vals[~frb.get_mask(\"dim_theta\")] = np.nan\n", + "fig = plt.figure(figsize=(6, 6))\n", + "plt.imshow(vals, extent=frb.bounds, origin=\"lower\", cmap=\"magma\")\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b88a426e-5411-421a-9dce-afb36fe67301", + "metadata": {}, + "source": [ + "### a note on \"edge tolerance\"\n", + "\n", + "The way the fixed resolution buffer is constructed involves converting from the in-plane x-y coordinates to spherical coordinates. This introduces a bit of error, and when calculating whether a point on the image buffer falls within a spherical element, sometimes that small error causes that check to fail. \n", + "\n", + "To avoid this issue, a small tolerance factor, `edge_tol` is added within the pixelization routine. Depending on the size of a grid's elements and the resolution of the image buffer, this tolerance may need to be adjusted so it is exposed as a keyword argument to `ds.cutting_mixed`. \n", + "\n", + "We can demonstrate it's effect by setting the `edge_tol` to 0.0: " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f470fd5f-8e7c-4e6f-b610-c932558a4199", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "slc = ds.cartesian_cutting(normal, center, north_vector=north_vector, edge_tol=0.0)\n", + "frb = slc.to_frb(0.6, 600)\n", + "vals = frb[\"dim_theta\"]\n", + "vals[~frb.get_mask(\"dim_theta\")] = np.nan\n", + "\n", + "fig = plt.figure(figsize=(6, 6))\n", + "plt.imshow(vals, extent=frb.bounds, origin=\"lower\", cmap=\"magma\")\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "517138a1-89b5-4198-af95-d3eb3b39d0fe", + "metadata": {}, + "source": [ + "The blank pixels are spots where the rounding error cause a pixel to fail a bounds check for an element. Increasing the edge tolerance will fill in those pixels (the default is 1e-12)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e994ae71-617a-48e0-bfe1-b76c9e5b7f91", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "slc = ds.cartesian_cutting(normal, center, north_vector=north_vector, edge_tol=1e-8)\n", + "frb = slc.to_frb(0.6, 600)\n", + "vals = frb[\"dim_theta\"]\n", + "vals[~frb.get_mask(\"dim_theta\")] = np.nan\n", + "\n", + "fig = plt.figure(figsize=(6, 6))\n", + "plt.imshow(vals, extent=frb.bounds, origin=\"lower\", cmap=\"magma\")\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0ad1308e-21dd-44d6-a7ff-68bb3a1b81f9", + "metadata": {}, + "source": [ + "increasing it **too** much will start to blur adjacent elements and find values where there shouldn't be:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9134ba55-9d47-4849-9920-1ae6ab9983a8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "slc = ds.cartesian_cutting(normal, center, north_vector=north_vector, edge_tol=1e-1)\n", + "frb = slc.to_frb(0.6, 600)\n", + "vals = frb[\"dim_theta\"]\n", + "vals[~frb.get_mask(\"dim_theta\")] = np.nan\n", + "\n", + "fig = plt.figure(figsize=(6, 6))\n", + "plt.imshow(vals, extent=frb.bounds, origin=\"lower\", cmap=\"magma\")\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "64400b46-e545-4f0a-a911-b006268216e8", + "metadata": {}, + "source": [ + "## example with an actual (sample) dataset\n", + "\n", + "Now let's try it out using an actual dataset, KeplerianDisk." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c3018907-8ad2-4ba9-adb3-75a91e90f7fd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:42,784 Sample dataset found in '/home/chavlin/hdd/data/yt_data/yt_sample_sets/KeplerianDisk/disk.out1.00000.athdf'\n", + "yt : [WARNING ] 2024-03-28 15:23:42,924 Assuming 1.0 = 1.0 cm\n", + "yt : [WARNING ] 2024-03-28 15:23:42,925 Assuming 1.0 = 1.0 s\n", + "yt : [WARNING ] 2024-03-28 15:23:42,926 Assuming 1.0 = 1.0 g\n", + "yt : [WARNING ] 2024-03-28 15:23:42,926 Assuming 1.0 = 1.0 K\n", + "yt : [INFO ] 2024-03-28 15:23:42,983 Parameters: current_time = 0.0\n", + "yt : [INFO ] 2024-03-28 15:23:42,983 Parameters: domain_dimensions = [256 64 4]\n", + "yt : [INFO ] 2024-03-28 15:23:42,984 Parameters: domain_left_edge = [0.3 1.17809725 0. ]\n", + "yt : [INFO ] 2024-03-28 15:23:42,984 Parameters: domain_right_edge = [3. 1.96349541 6.28318531]\n", + "yt : [INFO ] 2024-03-28 15:23:42,985 Parameters: cosmological_simulation = 0\n" + ] + } + ], + "source": [ + "ds = yt.load_sample(\"KeplerianDisk\")" + ] + }, + { + "cell_type": "markdown", + "id": "e4b29e1b-5d3b-4056-96e0-1157e161440b", + "metadata": {}, + "source": [ + "for reference, a phi-normal slice at phi=0, normal to the x-z plane looks like" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8f7b8f7f-a83d-4fb1-ab4c-5f811faf4672", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:43,806 xlim = 0.277164 3.000000\n", + "yt : [INFO ] 2024-03-28 15:23:43,806 ylim = -1.148050 1.148050\n", + "yt : [INFO ] 2024-03-28 15:23:43,807 Setting origin='native' for spherical geometry.\n", + "yt : [INFO ] 2024-03-28 15:23:43,809 xlim = 0.277164 3.000000\n", + "yt : [INFO ] 2024-03-28 15:23:43,810 ylim = -1.148050 1.148050\n", + "yt : [INFO ] 2024-03-28 15:23:43,811 Making a fixed resolution buffer of (('gas', 'density')) 800 by 800\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yt.SlicePlot(ds, \"phi\", \"density\", center=ds.arr([0.0, 0.0, 0.0], \"code_length\"))" + ] + }, + { + "cell_type": "markdown", + "id": "9a871c0a-c789-477e-bc19-12efeb132644", + "metadata": {}, + "source": [ + "now with the mixed-coord cutting plane, but spanning both phi = 0 and pi" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "15df32e7-639a-4142-afa3-5e7104a41fe7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "yt : [INFO ] 2024-03-28 15:23:46,834 Making a fixed resolution buffer of (('athena_pp', 'dens')) 800 by 800\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "normal = np.array([0.0, 1.0, 0.0])\n", + "plane_center = np.array([0.0, 0.0, 0.0])\n", + "slc = ds.cartesian_cutting(normal, plane_center)\n", + "\n", + "frb = slc.to_frb(7.0, 800, height=4.0)\n", + "bvals = frb[(\"athena_pp\", \"dens\")]\n", + "\n", + "# mask out empty values for plotting\n", + "mask = frb.get_mask((\"athena_pp\", \"dens\"))\n", + "bvals[~mask] = np.nan\n", + "\n", + "f = plt.figure(figsize=(8, 4))\n", + "plt.imshow(np.log10(bvals), extent=frb.bounds, origin=\"lower\", cmap=\"viridis\")\n", + "plt.xlabel(\"in-plane x\")\n", + "plt.ylabel(\"in-plane y\")\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "id": "407099b5-f240-45fc-971b-1ad796ec264d", + "metadata": {}, + "source": [ + "and now a slice parallel to the x-y plane, centered at (0,0,0.5):" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a1e88d62-7a44-4bd2-990d-7292b5db2f6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "normal = np.array([0.0, 0.0, 1.0])\n", + "plane_center = np.array([0.0, 0.0, 0.5])\n", + "slc = ds.cartesian_cutting(normal, plane_center)\n", + "frb = slc.to_frb(7.0, 800)\n", + "\n", + "bvals = frb[(\"athena_pp\", \"dens\")]\n", + "mask = frb.get_mask((\"athena_pp\", \"dens\"))\n", + "bvals[~mask] = np.nan\n", + "\n", + "# plot it\n", + "f = plt.figure(figsize=(8, 4))\n", + "plt.imshow(np.log10(bvals), extent=frb.bounds, origin=\"lower\")\n", + "plt.xlabel(\"in-plane x\")\n", + "plt.ylabel(\"in-plane y\")\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cedb6365-018a-47dd-9464-59943abe1585", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nose_ignores.txt b/nose_ignores.txt index 09ad4a12598..69fccea1698 100644 --- a/nose_ignores.txt +++ b/nose_ignores.txt @@ -1,6 +1,8 @@ --ignore-file=test_add_field\.py --ignore-file=test_ambiguous_fields\.py --ignore-file=test_callable_grids\.py +--ignore-file=test_cartesian_cutting_plane\.py +--ignore-file=test_cartesian_cutting_plane_selector\.py --ignore-file=test_callbacks_geographic\.py --ignore-file=test_commons\.py --ignore-file=test_cython_fortran_utils\.py diff --git a/tests/tests.yaml b/tests/tests.yaml index 5ef62862ac7..eed90728ad1 100644 --- a/tests/tests.yaml +++ b/tests/tests.yaml @@ -172,6 +172,8 @@ other_tests: - "--ignore-file=test_add_field\\.py" - "--ignore-file=test_ambiguous_fields\\.py" - "--ignore-file=test_callable_grids\\.py" + - "--ignore-file=test_cartesian_cutting_plane\\.py" + - "--ignore-file=test_cartesian_cutting_plane_selector\\.py" - "--ignore-file=test_callbacks_geographic\\.py" - "--ignore-file=test_commons\\.py" - "--ignore-file=test_cython_fortran_utils\\.py" diff --git a/yt/data_objects/selection_objects/data_selection_objects.py b/yt/data_objects/selection_objects/data_selection_objects.py index 58a0b39d97f..0dbda05cc30 100644 --- a/yt/data_objects/selection_objects/data_selection_objects.py +++ b/yt/data_objects/selection_objects/data_selection_objects.py @@ -72,12 +72,16 @@ def __init__(self, ds, field_parameters, data_source=None): self.field_parameters.update(data_source.field_parameters) self.quantities = DerivedQuantityCollection(self) + def _get_selector_class(self): + s_module = getattr(self, "_selector_module", yt.geometry.selection_routines) + sclass = getattr(s_module, f"{self._type_name}_selector", None) + return sclass + @property def selector(self): if self._selector is not None: return self._selector - s_module = getattr(self, "_selector_module", yt.geometry.selection_routines) - sclass = getattr(s_module, f"{self._type_name}_selector", None) + sclass = self._get_selector_class() if sclass is None: raise YTDataSelectorNotImplemented(self._type_name) diff --git a/yt/data_objects/selection_objects/slices.py b/yt/data_objects/selection_objects/slices.py index 9bb2965604e..5858a02cb28 100644 --- a/yt/data_objects/selection_objects/slices.py +++ b/yt/data_objects/selection_objects/slices.py @@ -15,6 +15,8 @@ validate_object, validate_width_tuple, ) +from yt.geometry import selection_routines +from yt.geometry.geometry_enum import Geometry from yt.utilities.exceptions import YTNotInsideNotebook from yt.utilities.minimal_representation import MinimalSliceData from yt.utilities.orientation import Orientation @@ -169,7 +171,6 @@ class YTCuttingPlane(YTSelectionContainer2D): data_source: optional Draw the selection from the provided data source rather than all data associated with the dataset - Notes ----- @@ -233,13 +234,20 @@ def __init__( def normal(self): return self._norm_vec + def _current_chunk_xyz(self): + x = self._current_chunk.fcoords[:, 0] + y = self._current_chunk.fcoords[:, 1] + z = self._current_chunk.fcoords[:, 2] + return x, y, z + def _generate_container_field(self, field): if self._current_chunk is None: self.index._identify_base_chunk(self) if field == "px": - x = self._current_chunk.fcoords[:, 0] - self.center[0] - y = self._current_chunk.fcoords[:, 1] - self.center[1] - z = self._current_chunk.fcoords[:, 2] - self.center[2] + x, y, z = self._current_chunk_xyz() + x = x - self.center[0] + y = y - self.center[1] + z = z - self.center[2] tr = np.zeros(x.size, dtype="float64") tr = self.ds.arr(tr, "code_length") tr += x * self._x_vec[0] @@ -247,9 +255,10 @@ def _generate_container_field(self, field): tr += z * self._x_vec[2] return tr elif field == "py": - x = self._current_chunk.fcoords[:, 0] - self.center[0] - y = self._current_chunk.fcoords[:, 1] - self.center[1] - z = self._current_chunk.fcoords[:, 2] - self.center[2] + x, y, z = self._current_chunk_xyz() + x = x - self.center[0] + y = y - self.center[1] + z = z - self.center[2] tr = np.zeros(x.size, dtype="float64") tr = self.ds.arr(tr, "code_length") tr += x * self._y_vec[0] @@ -257,9 +266,10 @@ def _generate_container_field(self, field): tr += z * self._y_vec[2] return tr elif field == "pz": - x = self._current_chunk.fcoords[:, 0] - self.center[0] - y = self._current_chunk.fcoords[:, 1] - self.center[1] - z = self._current_chunk.fcoords[:, 2] - self.center[2] + x, y, z = self._current_chunk_xyz() + x = x - self.center[0] + y = y - self.center[1] + z = z - self.center[2] tr = np.zeros(x.size, dtype="float64") tr = self.ds.arr(tr, "code_length") tr += x * self._norm_vec[0] @@ -353,6 +363,7 @@ def to_frb(self, width, resolution, height=None, periodic=False): >>> frb = cutting.to_frb((1.0, "pc"), 1024) >>> write_image(np.log10(frb["gas", "density"]), "density_1pc.png") """ + if is_sequence(width): validate_width_tuple(width) width = self.ds.quan(width[0], width[1]) @@ -363,8 +374,150 @@ def to_frb(self, width, resolution, height=None, periodic=False): height = self.ds.quan(height[0], height[1]) if not is_sequence(resolution): resolution = (resolution, resolution) + from yt.visualization.fixed_resolution import FixedResolutionBuffer bounds = (-width / 2.0, width / 2.0, -height / 2.0, height / 2.0) frb = FixedResolutionBuffer(self, bounds, resolution, periodic=periodic) return frb + + +class YTCartesianCuttingPlane(YTCuttingPlane): + """ + A YTCartesianCuttingPlane (ds.cartesian_cutting) is similar to YTCuttingPlane (ds.cutting) + but the cutting plane is always defined in cartesian coordinates, allowing arbitrary slices + through datasets defined in non-cartesian geometries. + + Parameters + ---------- + normal : array_like + The vector that defines the desired plane in cartesian coordinates. + center : array_like + The center of the cutting plane, where the normal vector is anchored, in + cartesian coordinates. + north_vector: array_like, optional + An optional vector to describe the north-facing direction in the resulting + plane, in cartesian coordinates. + ds: ~yt.data_objects.static_output.Dataset, optional + An optional dataset to use rather than self.ds + field_parameters : dictionary + A dictionary of field parameters than can be accessed by derived + fields. + data_source: optional + Draw the selection from the provided data source rather than + all data associated with the dataset + edge_tol: float + Optional edge tolerance (default 1e-12). This controls the fuzziness + of element-plane intersection to account for floating point errors + in coordinate transformations. If your slice is missing elements, + try increasing this number a bit. + """ + + _type_name = "cartesian_cutting" + _con_args = ("normal", "center") + _tds_attrs = ("_inv_mat",) + _tds_fields = ("x", "y", "z", "dx") + _container_fields = ("px", "py", "pz", "pdx", "pdy", "pdz") + _supported_geometries = (Geometry.SPHERICAL,) + + def __init__( + self, + normal, + center, + north_vector=None, + ds=None, + field_parameters=None, + data_source=None, + edge_tol=1e-12, + ): + super().__init__( + normal, + center, + north_vector=north_vector, + ds=ds, + field_parameters=field_parameters, + data_source=data_source, + ) + self._ds_geom = self.ds.geometry + self._validate_geometry() + self.edge_tol = edge_tol + + def _validate_geometry(self): + if self._ds_geom not in self._supported_geometries: + if self._ds_geom is Geometry.CARTESIAN: + msg = ( + "YTCuttingPlaneMixedCoords is not supported for cartesian " + "coordinates: use YTCuttingPlane instead (i.e., ds.cutting)." + ) + raise NotImplementedError(msg) + else: + self._raise_unsupported_geometry() + + def _raise_unsupported_geometry(self): + msg = ( + "YTCuttingPlaneMixedCoords only supports the following " + f"geometries: {self._supported_geometries}. The current" + f" geometry is {self._ds_geom}." + ) + raise NotImplementedError(msg) + + @property + def _index_fields(self): + # note: using the default axis order here because the index fields + # will are accessed by-chunk and passed down to the pixelizer + # with an expected ordering matching the default ordering. + ax_order = self.ds.coordinates._default_axis_order + fields = [("index", fld) for fld in ax_order] + fields += [("index", f"d{fld}") for fld in ax_order] + return fields + + @property + def _cartesian_to_native(self): + if self._ds_geom is Geometry.SPHERICAL: + from yt.utilities.lib.coordinate_utilities import cartesian_to_spherical + + return cartesian_to_spherical + self._raise_unsupported_geometry() + + @property + def _native_to_cartesian(self): + if self._ds_geom is Geometry.SPHERICAL: + from yt.utilities.lib.coordinate_utilities import spherical_to_cartesian + + return spherical_to_cartesian + self._raise_unsupported_geometry() + + def _plane_coords(self, in_plane_x, in_plane_y): + # calculates the 3d coordinates of points on the plane in the + # native coordinate system of the dataset. + + # actual x, y, z locations of each point in the plane + c = self.center.d + x_global = in_plane_x * self._x_vec[0] + in_plane_y * self._y_vec[0] + c[0] + y_global = in_plane_x * self._x_vec[1] + in_plane_y * self._y_vec[1] + c[1] + z_global = in_plane_x * self._x_vec[2] + in_plane_y * self._y_vec[2] + c[2] + + # now transform to the native coordinates + return self._cartesian_to_native(x_global, y_global, z_global) + + def to_pw(self, fields=None, center="center", width=None, axes_unit=None): + msg = ( + "to_pw is not implemented for mixed coordinate slices. You can create" + " plots manually using to_frb() to generate a fixed resolution array." + ) + raise NotImplementedError(msg) + + def _get_selector_class(self): + s_module = getattr(self, "_selector_module", selection_routines) + if self.ds.geometry is Geometry.SPHERICAL: + type_name = self._type_name + "_spherical" + else: + self._raise_unsupported_geometry() + sclass = getattr(s_module, f"{type_name}_selector", None) + return sclass + + def _current_chunk_xyz(self): + x = self._current_chunk.fcoords[:, 0] + y = self._current_chunk.fcoords[:, 1] + z = self._current_chunk.fcoords[:, 2] + return self._native_to_cartesian(x, y, z) diff --git a/yt/data_objects/tests/test_cartesian_cutting_plane.py b/yt/data_objects/tests/test_cartesian_cutting_plane.py new file mode 100644 index 00000000000..7271aa287ac --- /dev/null +++ b/yt/data_objects/tests/test_cartesian_cutting_plane.py @@ -0,0 +1,114 @@ +import itertools + +import matplotlib.pyplot as plt +import numpy as np +import pytest +import unyt + +import yt +from yt.geometry.coordinates.spherical_coordinates import spherical_to_cartesian +from yt.testing import fake_amr_ds + + +def test_cartesian_cutting_plane(): + ds = fake_amr_ds(geometry="spherical") + normal = np.array([0.0, 0.0, 1.0]) + plane_center = np.array([0.0, 0.0, 0.5]) + slc = ds.cartesian_cutting(normal, plane_center) + frb = slc.to_frb(2.0, 800) + bvals = frb[("index", "r")] + mask = frb.get_mask(("index", "r")) + # note: the min value of r on the plane will be the z value of the + # plane center. how close it is to the correct answer will depend + # on the size of the elements. + assert np.allclose(bvals[mask].min().d, plane_center[2], atol=0.02) + + +def _get_spherical_uniform_grid(shp, bbox, axis_order): + + data = {"density": np.random.random(shp)} + + def _z(field, data): + r = data["index", "r"] + theta = data["index", "theta"] + phi = data["index", "phi"] + _, _, z = spherical_to_cartesian(r, theta, phi) + return unyt.unyt_array(z, r.units) + + ds = yt.load_uniform_grid( + data, + shp, + bbox=bbox, + geometry="spherical", + axis_order=axis_order, + length_unit="m", + ) + + ds.add_field( + name=("index", "z_val"), function=_z, sampling_type="cell", take_log=False + ) + return ds + + +@pytest.fixture +def spherical_ds(): + + shp = (32, 32, 32) + bbox = np.array([[0.0, 1.0], [0, np.pi], [0, 2 * np.pi]]) + ax_order = ("r", "theta", "phi") + return _get_spherical_uniform_grid(shp, bbox, ax_order) + + +def test_cartesian_cutting_plane_fixed_z(spherical_ds): + ds = spherical_ds + normal = np.array([0.0, 0.0, 1.0]) + center = np.array([0.0, 0.0, 0.5]) + slc = ds.cartesian_cutting(normal, center) + zvals = slc["index", "z_val"].to("code_length").d + assert np.allclose(zvals, ds.quan(0.5, "code_length").d, atol=0.05) + + +@pytest.mark.mpl_image_compare +def test_vertical_slice_at_sphere_edge(spherical_ds): + ds = spherical_ds + normal = np.array([0.0, 1.0, 0.0]) + center = np.array([0.0, 0.75, 0.0]) + slc = ds.cartesian_cutting(normal, center) + frb = slc.to_frb(2.0, 50) + vals = frb["index", "z_val"].to("code_length") + vals[~frb.get_mask(("index", "z_val"))] = np.nan + + f, axs = plt.subplots(1) + axs.imshow(vals, origin="lower", extent=frb.bounds) + return f + + +def test_cartesian_cutting_plane_with_axis_ordering(): + # check that slicing works with any axis order + shp = (32, 32, 32) + axes = ["r", "theta", "phi"] + bbox_ranges = {"r": [0.0, 1.0], "theta": [0, np.pi], "phi": [0, 2 * np.pi]} + + # set the attributes for the plane, including a north vector found + # for an arbitrary point on the plane. + normal = np.array([1.0, 1.0, 1.0]) + center = np.array([0.0, 0.0, 0.0]) + x, y = 1.0, 1.0 + z = -x * normal[0] - y * normal[1] + north_pt = np.array([x, y, z]) + assert np.dot(normal, north_pt) == 0.0 # just to be sure... + + frb_vals = [] + for axis_order in itertools.permutations(axes): + bbox = np.zeros((3, 2)) + for i, ax in enumerate(axis_order): + bbox[i, :] = bbox_ranges[ax] + ds = _get_spherical_uniform_grid(shp, bbox, tuple(axis_order)) + slc = ds.cartesian_cutting(normal, center, north_vector=north_pt) + frb = slc.to_frb(2.0, 50) + vals = frb["index", "z_val"].to("code_length") + vals[~frb.get_mask(("index", "z_val"))] = np.nan + frb_vals.append(vals.d) + + for frb_z in frb_vals[1:]: + np.allclose(frb_z, frb_vals[0]) diff --git a/yt/geometry/_selection_routines/cutting_plane_selector.pxi b/yt/geometry/_selection_routines/cutting_plane_selector.pxi index 45a0026b69d..11023910668 100644 --- a/yt/geometry/_selection_routines/cutting_plane_selector.pxi +++ b/yt/geometry/_selection_routines/cutting_plane_selector.pxi @@ -1,6 +1,9 @@ +from yt.utilities.lib.coordinate_utilities cimport _spherical_to_cartesian, _cartesian_to_spherical +from numpy.math cimport PI as NPY_PI + cdef class CuttingPlaneSelector(SelectorObject): - cdef public np.float64_t norm_vec[3] - cdef public np.float64_t d + cdef public np.float64_t norm_vec[3] # the unit-normal for the plane + cdef public np.float64_t d # the shortest distance from plane to origin def __init__(self, dobj): cdef int i @@ -35,11 +38,17 @@ cdef class CuttingPlaneSelector(SelectorObject): if height*height <= radius*radius : return 1 return 0 + @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) cdef int select_bbox(self, np.float64_t left_edge[3], np.float64_t right_edge[3]) noexcept nogil: + # the bbox selection here works by calculating the signed-distance from + # the plane to each vertex of the bounding box. If there is no + # intersection, the signed-distance for every vertex will have the same + # sign whereas if the sign flips then the plane must intersect the + # bounding box. cdef int i, j, k, n cdef np.float64_t *arr[2] cdef np.float64_t pos[3] @@ -70,6 +79,7 @@ cdef class CuttingPlaneSelector(SelectorObject): return 0 return 1 + @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) @@ -114,4 +124,189 @@ cdef class CuttingPlaneSelector(SelectorObject): def _get_state_attnames(self): return ("d", "norm_vec") + +cdef class CartesianCuttingPlaneBase(CuttingPlaneSelector): + # a base class for cartesian cutting planes through data that is not + # in cartesian coordinates. + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void transform_vertex_pos(self, np.float64_t pos_in[3], np.float64_t pos_out[3]) noexcept nogil: + # child class must implement: must transform from dataset native + # coordinates to cartesian coordinates (with (x,y,z) ordering) + pass + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef int select_bbox(self, np.float64_t left_edge[3], + np.float64_t right_edge[3]) noexcept nogil: + # child classes may over-ride if needed + cdef np.int64_t n_points[3] + n_points[0] = 2 + n_points[1] = 2 + n_points[2] = 2 + return self._select_bbox(left_edge, right_edge, n_points) + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef int _select_bbox(self, + np.float64_t left_edge[3], + np.float64_t right_edge[3], + np.int64_t n_points[3], + ) noexcept nogil: + + # the bbox selection here works by calculating the signed-distance from + # the plane to each vertex of the bounding box. If there is no + # intersection, the signed-distance for every vertex will have the same + # sign whereas if the sign flips then the plane must intersect the + # bounding box. + # + # left_edge, right_edge + # the left/right bounds of the bounding box + # n_points : + # the number of points to check in each dimension. + # (2,2,2) is equivalent to checking the corners of the + # bounding box. + + cdef int i, j, k, n + cdef np.float64_t pos[3] + cdef np.float64_t dpos[3] + cdef np.float64_t pos_cart[3] + cdef np.float64_t gd, n_pts + + for i in range(3): + dpos[i] = (right_edge[i] - left_edge[i]) / ( n_points[i] - 1.0) + + all_under = 1 + all_over = 1 + + for i in range(n_points[0]): + pos[0] = left_edge[0] + i * dpos[0] + for j in range(n_points[1]): + pos[1] = left_edge[1] + j * dpos[1] + for k in range(n_points[2]): + pos[2] = left_edge[2] + k * dpos[2] + self.transform_vertex_pos(pos, pos_cart) + gd = self.d + for n in range(3): + gd += pos_cart[n] * self.norm_vec[n] + # this allows corners and faces on the low-end to + # collide, while not selecting cells on the high-side + if i == 0 and j == 0 and k == 0 : + if gd <= 0: all_over = 0 + if gd >= 0: all_under = 0 + else : + if gd < 0: all_over = 0 + if gd > 0: all_under = 0 + + if all_over == 1 or all_under == 1: + return 0 + return 1 + + +cdef class CartesianCuttingPlaneSpherical(CartesianCuttingPlaneBase): + + # intersection of a cartesian plane with data in spherical coordinates. + # expected ordering is (r, theta, phi), where theta is the colatitude + # angle (bounds of 0 to pi) and phi is the azimuthal/longitudinal + # angle (bounds 0 to 2pi). + + cdef public np.float64_t r_min # the minimum radius for possible intersection + cdef public np.float64_t c_rtp[3] + + def __init__(self, dobj): + + cdef np.float64_t xyz[3] + cdef int i + super().__init__(dobj) + + # any points at r < |d|, where d is the minimum distance-vector to the + # plane, cannot intersect the plane. Record r_min here for convenience: + self.r_min = fabs(self.d) + + # also record the spherical coordinates of the point on the plane + # closest to the origin + for i in range(3): + xyz[i] = - self.norm_vec[i] * self.d # cartesian position + self.c_rtp[0], self.c_rtp[1], self.c_rtp[2] = _cartesian_to_spherical(xyz[0], xyz[1], xyz[2]) + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef void transform_vertex_pos(self, np.float64_t pos_in[3], np.float64_t pos_out[3]) noexcept nogil: + # r => in_pos[0] theta => in_pos[1] phi => in_pos[2] + pos_out[0], pos_out[1], pos_out[2] = _spherical_to_cartesian(pos_in[0], pos_in[1], pos_in[2]) + + + @cython.boundscheck(False) + @cython.wraparound(False) + @cython.cdivision(True) + cdef int select_bbox(self, + np.float64_t left_edge[3], + np.float64_t right_edge[3]) noexcept nogil: + # left/right edge here are in spherical coordinates in (r, theta, phi) + + cdef int selected, idim + cdef np.float64_t left_edge_c[3], right_edge_c[3], + cdef np.int64_t n_points[3] + cdef np.float64_t NPY_PI_4 = NPY_PI / 4.0 + cdef np.float64_t dangle, PI2 + + + # set the number of points to check depending on angular range + # of element: small elements will only check the bounding box + # verts as normal. elements with large angular range, > pi/4, will + # add additional vertices. + for idim in range(3): + n_points[idim] = 2 + # check the angular ranges and add extra points if too large... + for idim in range(1,3): + dangle = right_edge[idim] - left_edge[idim] + if dangle >= NPY_PI_4: + n_points[idim] = 2 + (dangle / NPY_PI_4) + + # run the plane-vertex distance check (vertex positions are converted to + # cartesian within _select_bbox). + selected = self._select_bbox(left_edge, right_edge, n_points) + + if selected == 0: + # there is one more special case to consider! + # When all vertices lie on one side of the plane, intersection + # is still possible if the plane intersects the outer cusp of the + # spherical volume element. **BUT** if we've reached this far, + # the only way for this to happen is if the position of the point + # on the plane that is closest to the origin lies within the + # element itself. + if self.c_rtp[0] > left_edge[0]: + for idim in range(1,3): + if self.c_rtp[idim] <= left_edge[idim]: + return 0 + if self.c_rtp[idim] >= right_edge[idim]: + return 0 + return 1 + + return selected + + def _select_single_bbox(self, + left_edge_in, + right_edge_in): + + # useful for direct testing without having to initialize + # full yt data objects + + cdef np.float64_t left_edge[3] + cdef np.float64_t right_edge[3] + + for i in range(3): + left_edge[i] = left_edge_in[i] + right_edge[i] = right_edge_in[i] + + return self.select_bbox(left_edge, right_edge) + + cutting_selector = CuttingPlaneSelector + +cartesian_cutting_spherical_selector = CartesianCuttingPlaneSpherical diff --git a/yt/geometry/coordinates/spherical_coordinates.py b/yt/geometry/coordinates/spherical_coordinates.py index 165287393a2..ef3bd9c993e 100644 --- a/yt/geometry/coordinates/spherical_coordinates.py +++ b/yt/geometry/coordinates/spherical_coordinates.py @@ -2,7 +2,12 @@ import numpy as np -from yt.utilities.lib.pixelization_routines import pixelize_aitoff, pixelize_cylinder +from yt.utilities.lib.coordinate_utilities import spherical_to_cartesian +from yt.utilities.lib.pixelization_routines import ( + pixelize_aitoff, + pixelize_cylinder, + pixelize_off_axis_mixed_coords, +) from .coordinate_handler import ( CoordinateHandler, @@ -94,7 +99,7 @@ def pixelize( return_mask=False, ): self.period - name = self.axis_name[dimension] + name = self.axis_name.get(dimension, dimension) if name == "r": buff, mask = self._ortho_pixelize( data_source, field, bounds, size, antialias, dimension, periodic @@ -112,7 +117,9 @@ def pixelize( data_source, field, bounds, size, antialias, dimension ) else: - raise NotImplementedError + buff, mask = self._oblique_pixelize( + data_source, field, bounds, size, antialias, dimension + ) if return_mask: assert mask is None or mask.dtype == bool @@ -123,6 +130,68 @@ def pixelize( def pixelize_line(self, field, start_point, end_point, npoints): raise NotImplementedError + def _oblique_pixelize( + self, + data_source, + field, + bounds, + size, + antialias, + dimension, + ): + + from yt.utilities.lib.coordinate_utilities import ( + SphericalMixedCoordBBox, + ) + + buff = np.zeros(size) + + def _1d_sample_points(bounds, buff_size, axisid): + # get a 1d array of sample points along a dimension + bmin_i = bounds[axisid * 2] + bmax_i = bounds[axisid * 2 + 1] + buff_size_i = buff_size[axisid] + dx_i = (bmax_i - bmin_i) / buff_size_i + x_i = bmin_i + dx_i / 2.0 + np.arange(buff_size_i) * dx_i + return x_i + + # get the coordinates of the plane in the coordinate system of the + # underlying dataset (the "native" coordinates) + x_plane = _1d_sample_points(bounds, size, 0) + y_plane = _1d_sample_points(bounds, size, 1) + # in-plane x-y coordinates of each pixel in buffer + b_x, b_y = np.meshgrid(x_plane, y_plane, indexing="ij") + # spherical coords if each pixel in buffer + b_r, b_theta, b_phi = data_source._plane_coords(b_x, b_y) + + bbox_handler = SphericalMixedCoordBBox() + + indxs = np.arange(0, data_source[("index", "r")].size) + mask = pixelize_off_axis_mixed_coords( + bbox_handler, + buff, + b_r, + b_theta, + b_phi, + data_source[("index", "r")].astype(np.float64), + data_source[("index", "theta")].astype(np.float64), + data_source[("index", "phi")].astype(np.float64), + data_source[("index", "dr")].astype(np.float64), + data_source[("index", "dtheta")].astype(np.float64), + data_source[("index", "dphi")].astype(np.float64), + data_source.center, + data_source._norm_vec, + data_source._x_vec, + data_source._y_vec, + indxs, + data_source[field].astype(np.float64), + bounds, + return_mask=1, + edge_tol=data_source.edge_tol, + ) + + return buff.T, mask.T + def _ortho_pixelize( self, data_source, field, bounds, size, antialias, dim, periodic ): @@ -185,19 +254,9 @@ def convert_to_cartesian(self, coord): r = coord[:, ri] theta = coord[:, thetai] phi = coord[:, phii] - nc = np.zeros_like(coord) - # r, theta, phi - nc[:, ri] = np.cos(phi) * np.sin(theta) * r - nc[:, thetai] = np.sin(phi) * np.sin(theta) * r - nc[:, phii] = np.cos(theta) * r else: r, theta, phi = coord - nc = ( - np.cos(phi) * np.sin(theta) * r, - np.sin(phi) * np.sin(theta) * r, - np.cos(theta) * r, - ) - return nc + return spherical_to_cartesian(r, theta, phi) def convert_to_cylindrical(self, coord): raise NotImplementedError diff --git a/yt/geometry/tests/test_cartesian_cutting_plane_selector.py b/yt/geometry/tests/test_cartesian_cutting_plane_selector.py new file mode 100644 index 00000000000..f13c441a03e --- /dev/null +++ b/yt/geometry/tests/test_cartesian_cutting_plane_selector.py @@ -0,0 +1,146 @@ +import numpy as np +import pytest + +from yt import load_uniform_grid +from yt.geometry.selection_routines import cartesian_cutting_spherical_selector +from yt.testing import fake_amr_ds + + +class HelpfulPlaneObject: + # a bare-bones skeleton of a data object to use for initializing + # the cython cutting plane selector + def __init__(self, normal, plane_center): + self._d = -1 * np.dot(normal, plane_center) + self._norm_vec = normal + + +@pytest.fixture +def xy_plane_at_001(): + normal = np.array([0.0, 0.0, 1.0]) + plane_center = np.array([0.0, 0.0, 1.0]) + return HelpfulPlaneObject(normal, plane_center) + + +def _in_rads(x): + return x * np.pi / 180 + + +def test_spherical_cutting_plane_spots(xy_plane_at_001): + # a couple of manual spot checks for intersection of a plane + # with some spherical volume elements + + # initialize selector + scp = cartesian_cutting_spherical_selector(xy_plane_at_001) + assert scp.r_min == 1.0 + + # left/right edge values are given in spherical coordinates with + # order of (r, theta, phi) where + # theta is the colatitude (bounds 0 to pi) + # phi is the azimuth (bounds 0 to 2pi). + + # should intersect + left_edge = np.array([0.8, _in_rads(5), _in_rads(5)]) + right_edge = np.array([1.2, _in_rads(45), _in_rads(45)]) + assert scp._select_single_bbox(left_edge, right_edge) + + # should not intersect + left_edge = np.array([0.1, _in_rads(90), _in_rads(5)]) + right_edge = np.array([0.4, _in_rads(110), _in_rads(45)]) + assert scp._select_single_bbox(left_edge, right_edge) == 0 + + +def test_large_angular_range(): + # check that large elements are still selected + + # these edges define a single element that is a spherical shell of finite + # thickness spanning a hemisphere. The bounds of the element all fall on + # one side of the test plane, so these checks rely on the additional angular + # verts that get added for large elements + left_edge = np.array([0.8, 0.01, 0.01]) + right_edge = np.array([1.0, np.pi - 0.01, np.pi - 0.01]) + + for y_pos in np.linspace(0.1, 0.99, 10): + normal = np.array([0.0, 1.0, 0.0]) + plane_center = np.array([0.0, y_pos, 0.0]) + xz_plane = HelpfulPlaneObject(normal, plane_center) + scp = cartesian_cutting_spherical_selector(xz_plane) + + selected = scp._select_single_bbox(left_edge, right_edge) + assert selected + + lev = np.array( + [ + [ + 0, + ] + ], + dtype=np.int32, + ) + left_edges = np.array( + [ + left_edge, + ] + ) + right_edges = np.array( + [ + right_edge, + ] + ) + grid_sel = scp.select_grids(left_edges, right_edges, lev) + assert grid_sel + + +def test_large_angular_range_ds(): + # checks that a ds in spherical coords with a single grid spanning + # a large angular range gets selected properly. + bbox = np.array([[0.5, 1.0], [0, np.pi], [0, np.pi]]) + + shp = (32,) * 3 + data = {"density": np.random.random(shp)} + + ds = load_uniform_grid( + data, + shp, + bbox=bbox, + geometry="spherical", + axis_order=("r", "theta", "phi"), + length_unit="m", + nprocs=1, + ) + + normal = ds.arr([0.0, 1.0, 0], "code_length") + center = ds.arr([0.0, 0.2, 0.0], "code_length") + slc = ds.cartesian_cutting(normal, center) + + le = ds.index.grid_left_edge + re = ds.index.grid_right_edge + lev = ds.index.grid_levels + selected = slc.selector.select_grids(le, re, lev) + assert np.all(selected) + + +def test_spherical_cutting_plane(xy_plane_at_001): + + ds = fake_amr_ds(geometry="spherical") + + # this plane will miss the dataset entirely + normal = np.array([0.0, 0.0, 1.0]) + plane_center = np.array([0.0, 0.0, 1.1]) + slc = ds.cartesian_cutting(normal, plane_center) + assert len(slc[("stream", "Density")]) == 0 + + # this one will not. + normal = np.array([0.0, 0.0, 1.0]) + plane_center = np.array([0.0, 0.0, 0.5]) + slc = ds.cartesian_cutting(normal, plane_center) + r = slc[("index", "r")] + theta = slc[("index", "theta")] + # r cannot be smaller than the distance from plane to origin + assert np.min(r) >= plane_center[2] + + # how close the z value is to the plane's z value + # depends on the size of the elements, the closeness here + # was found empirically + z = r * np.cos(theta) + max_z = np.max(np.abs(z.to("code_length").d - 0.5)) + assert np.isclose(max_z, 0.04212724) diff --git a/yt/utilities/lib/coordinate_utilities.pxd b/yt/utilities/lib/coordinate_utilities.pxd new file mode 100644 index 00000000000..e9cd62e9229 --- /dev/null +++ b/yt/utilities/lib/coordinate_utilities.pxd @@ -0,0 +1,37 @@ +cimport numpy as np + + +cdef (np.float64_t, np.float64_t, np.float64_t) _spherical_to_cartesian(np.float64_t r, + np.float64_t theta, + np.float64_t phi) noexcept nogil + + +cdef (np.float64_t, np.float64_t, np.float64_t) _cartesian_to_spherical(np.float64_t x, + np.float64_t y, + np.float64_t z) noexcept nogil + +cdef class MixedCoordBBox: + cdef void get_cartesian_bbox(self, + np.float64_t pos0, + np.float64_t pos1, + np.float64_t pos2, + np.float64_t dpos0, + np.float64_t dpos1, + np.float64_t dpos2, + np.float64_t xyz_i[3], + np.float64_t dxyz_i[3] + ) noexcept nogil + + +cdef class SphericalMixedCoordBBox(MixedCoordBBox): + cdef void get_cartesian_bbox( + self, + np.float64_t pos0, + np.float64_t pos1, + np.float64_t pos2, + np.float64_t dpos0, + np.float64_t dpos1, + np.float64_t dpos2, + np.float64_t xyz_i[3], + np.float64_t dxyz_i[3] + ) noexcept nogil diff --git a/yt/utilities/lib/coordinate_utilities.pyx b/yt/utilities/lib/coordinate_utilities.pyx new file mode 100644 index 00000000000..fdbfeb9516a --- /dev/null +++ b/yt/utilities/lib/coordinate_utilities.pyx @@ -0,0 +1,306 @@ +cimport cython +import numpy as np +cimport numpy as np + +from libc.math cimport cos, sin, atan2, acos, sqrt + +from numpy.math cimport PI as NPY_PI +from numpy.math cimport INFINITY as NPY_INF + +from yt.utilities.lib.fp_utils cimport fmax, fmin + + +@cython.cdivision(True) +@cython.boundscheck(False) +@cython.wraparound(False) +cdef (np.float64_t, np.float64_t, np.float64_t) _spherical_to_cartesian(np.float64_t r, + np.float64_t theta, + np.float64_t phi) noexcept nogil: + # transform a single point in spherical coords to cartesian + # r : radius + # theta: colatitude + # phi: azimuthal (longitudinal) angle + cdef np.float64_t x, y, xy, z + + if r == 0.0: + return 0.0, 0.0, 0.0 + + xy = r * sin(theta) + x = xy * cos(phi) + y = xy * sin(phi) + z = r * cos(theta) + return x, y, z + + +@cython.cdivision(True) +@cython.boundscheck(False) +@cython.wraparound(False) +cdef (np.float64_t, np.float64_t, np.float64_t) _cartesian_to_spherical(np.float64_t x, + np.float64_t y, + np.float64_t z) noexcept nogil: + # transform a single point in cartesian coords to spherical, returns + # r : radius + # theta: colatitude + # phi: azimuthal angle in range (0, 2pi) + cdef np.float64_t r, theta, phi + r = sqrt(x*x + y*y + z*z) + theta = acos(z / r) + phi = atan2(y, x) + # atan2 returns -pi to pi, adjust to (0, 2pi) + if phi < 0: + phi = phi + 2 * NPY_PI + return r, theta, phi + + +@cython.cdivision(True) +@cython.boundscheck(False) +@cython.wraparound(False) +def cartesian_to_spherical(np.ndarray x, + np.ndarray y, + np.ndarray z): + # transform an array of points in cartesian coords to spherical, returns + # r : radius + # theta: colatitude + # phi: azimuthal angle in range (0, 2pi) + cdef np.ndarray[np.float64_t, ndim=1] r1d, th1d, phi1d + cdef np.ndarray[np.float64_t, ndim=1] x1d, y1d, z1d + cdef int i, n_x, ndim + cdef np.int64_t[:] shp + + ndim = x.ndim + + shp = np.zeros((ndim,), dtype=np.int64) + for i in range(ndim): + shp[i] = x.shape[i] + + x1d = x.reshape(-1) + y1d = y.reshape(-1) + z1d = z.reshape(-1) + + n_x = x1d.size + r1d = np.zeros((n_x,), dtype=np.float64) + th1d = np.zeros((n_x,), dtype=np.float64) + phi1d = np.zeros((n_x,), dtype=np.float64) + + + with nogil: + for i in range(n_x): + r1d[i], th1d[i], phi1d[i] = _cartesian_to_spherical(x1d[i], y1d[i], z1d[i]) + + r = r1d.reshape(shp) + theta = th1d.reshape(shp) + phi = phi1d.reshape(shp) + return r, theta, phi + + +@cython.cdivision(True) +@cython.boundscheck(False) +@cython.wraparound(False) +def spherical_to_cartesian(np.ndarray r, + np.ndarray theta, + np.ndarray phi): + + # transform an array of points in spherical coords to cartesian + cdef np.ndarray[np.float64_t, ndim=1] r1d, th1d, phi1d + cdef np.ndarray[np.float64_t, ndim=1] x1d, y1d, z1d + + cdef np.int64_t[:] shp + cdef int i, n_r, ndim + + ndim = r.ndim + + shp = np.zeros((ndim,), dtype=np.int64) + for i in range(ndim): + shp[i] = r.shape[i] + + r1d = r.reshape(-1) + th1d = theta.reshape(-1) + phi1d = phi.reshape(-1) + + n_r = r1d.size + x1d = np.zeros((n_r,), dtype=np.float64) + y1d = np.zeros((n_r,), dtype=np.float64) + z1d = np.zeros((n_r,), dtype=np.float64) + + + with nogil: + for i in range(n_r): + x1d[i], y1d[i], z1d[i] = _spherical_to_cartesian(r1d[i], th1d[i], phi1d[i]) + + x = x1d.reshape(shp) + y = y1d.reshape(shp) + z = z1d.reshape(shp) + return x, y, z + + +cdef class MixedCoordBBox: + # abstract class for calculating cartesian bounding boxes + # from non-cartesian grid elements. + cdef void get_cartesian_bbox(self, + np.float64_t pos0, + np.float64_t pos1, + np.float64_t pos2, + np.float64_t dpos0, + np.float64_t dpos1, + np.float64_t dpos2, + np.float64_t xyz_i[3], + np.float64_t dxyz_i[3] + ) noexcept nogil: + pass + + +cdef class SphericalMixedCoordBBox(MixedCoordBBox): + # Cartesian bounding boxes of spherical grid elements + cdef void get_cartesian_bbox(self, + np.float64_t pos0, + np.float64_t pos1, + np.float64_t pos2, + np.float64_t dpos0, + np.float64_t dpos1, + np.float64_t dpos2, + np.float64_t xyz_i[3], + np.float64_t dxyz_i[3] + ) noexcept nogil: + + cdef np.float64_t r_i, theta_i, phi_i, dr_i, dtheta_i, dphi_i + cdef np.float64_t h_dphi, h_dtheta, h_dr, r_r + cdef np.float64_t xi, yi, zi, r_lr, theta_lr, phi_lr, phi_lr2, theta_lr2 + cdef np.float64_t xli, yli, zli, xri, yri, zri, r_xy, r_xy2 + cdef int isign_r, isign_ph, isign_th + cdef np.float64_t sign_r, sign_th, sign_ph + + cdef np.float64_t NPY_PI_2 = NPY_PI / 2.0 + cdef np.float64_t NPY_PI_3_2 = 3. * NPY_PI / 2.0 + cdef np.float64_t NPY_2xPI = 2. * NPY_PI + + r_i = pos0 + theta_i = pos1 + phi_i = pos2 + dr_i = dpos0 + dtheta_i = dpos1 + dphi_i = dpos2 + + # initialize the left/right values + xli = NPY_INF + yli = NPY_INF + zli = NPY_INF + xri = -1.0 * NPY_INF + yri = -1.0 * NPY_INF + zri = -1.0 * NPY_INF + + # find the min/max bounds over the 8 corners of the + # spherical volume element. + h_dphi = dphi_i / 2.0 + h_dtheta = dtheta_i / 2.0 + h_dr = dr_i / 2.0 + for isign_r in range(2): + for isign_ph in range(2): + for isign_th in range(2): + sign_r = 1.0 - 2.0 * isign_r + sign_th = 1.0 - 2.0 * isign_th + sign_ph = 1.0 - 2.0 * isign_ph + r_lr = r_i + sign_r * h_dr + theta_lr = theta_i + sign_th * h_dtheta + phi_lr = phi_i + sign_ph * h_dphi + + xi, yi, zi = _spherical_to_cartesian(r_lr, theta_lr, phi_lr) + + xli = fmin(xli, xi) + yli = fmin(yli, yi) + zli = fmin(zli, zi) + xri = fmax(xri, xi) + yri = fmax(yri, yi) + zri = fmax(zri, zi) + + # need to correct for special cases: + # if polar angle, phi, spans pi/2, pi or 3pi/2 then just + # taking the min/max of the corners will miss the cusp of the + # element. When this condition is met, the x/y min/max will + # equal +/- the projection of the max r in the xy plane -- in this case, + # the theta angle that gives the max projection of r in + # the x-y plane will change depending on the whether theta < or > pi/2, + # so the following calculates for the min/max theta value of the element + # and takes the max. + # ALSO note, that the following does check for when an edge aligns with the + # phi=0/2pi, it does not handle an element spanning the periodic boundary. + # Oh and this may break down for large elements that span whole + # quadrants... + phi_lr = phi_i - h_dphi + phi_lr2 = phi_i + h_dphi + theta_lr = theta_i - h_dtheta + theta_lr2 = theta_i + h_dtheta + r_r = r_i + h_dr + if theta_lr < NPY_PI_2 and theta_lr2 > NPY_PI_2: + r_xy = r_r + else: + r_xy = r_r * sin(theta_lr) + r_xy2 = r_r * sin(theta_lr2) + r_xy = fmax(r_xy, r_xy2) + + if phi_lr == 0.0 or phi_lr2 == NPY_2xPI: + # need to re-check this, for when theta spans equator + xri = r_xy + elif phi_lr < NPY_PI_2 and phi_lr2 > NPY_PI_2: + yri = r_xy + elif phi_lr < NPY_PI and phi_lr2 > NPY_PI: + xli = -r_xy + elif phi_lr < NPY_PI_3_2 and phi_lr2 > NPY_PI_3_2: + yli = -r_xy + + xyz_i[0] = (xri+xli)/2. + xyz_i[1] = (yri+yli)/2. + xyz_i[2] = (zri+zli)/2. + dxyz_i[0] = xri-xli + dxyz_i[1] = yri-yli + dxyz_i[2] = zri-zli + + +@cython.cdivision(True) +@cython.boundscheck(False) +@cython.wraparound(False) +def cartesian_bboxes(MixedCoordBBox bbox_handler, + np.float64_t[:] pos0, + np.float64_t[:] pos1, + np.float64_t[:] pos2, + np.float64_t[:] dpos0, + np.float64_t[:] dpos1, + np.float64_t[:] dpos2, + np.float64_t[:] x, + np.float64_t[:] y, + np.float64_t[:] z, + np.float64_t[:] dx, + np.float64_t[:] dy, + np.float64_t[:] dz, + ): + # calculates the cartesian bounding boxes around non-cartesian + # volume elements + # + # bbox_handler : a MixedCoordBBox child instance + # pos0, pos1, pos2: native coordinates of element centers + # dpos0, dpos1, dpos2: element widths in native coordinates + # x, y, z: cartesian centers of bounding boxes, modified in place + # dx, dy, dz : full-widths of the cartesian bounding boxes, modified in place + + cdef int i, n_pos + cdef np.float64_t xyz_i[3] + cdef np.float64_t dxyz_i[3] + + n_pos = pos0.size + with nogil: + for i in range(n_pos): + + bbox_handler.get_cartesian_bbox(pos0[i], + pos1[i], + pos2[i], + dpos0[i], + dpos1[i], + dpos2[i], + xyz_i, + dxyz_i) + + x[i] = xyz_i[0] + y[i] = xyz_i[1] + z[i] = xyz_i[2] + dx[i] = dxyz_i[0] + dy[i] = dxyz_i[1] + dz[i] = dxyz_i[2] diff --git a/yt/utilities/lib/pixelization_routines.pyx b/yt/utilities/lib/pixelization_routines.pyx index 8cc37ab1d12..6de85f18007 100644 --- a/yt/utilities/lib/pixelization_routines.pyx +++ b/yt/utilities/lib/pixelization_routines.pyx @@ -28,7 +28,6 @@ from yt.utilities.lib.fp_utils cimport ( i64min, iclip, ) - from yt.utilities.exceptions import YTElementTypeNotRecognized, YTPixelizeError from cpython.exc cimport PyErr_CheckSignals @@ -49,6 +48,7 @@ from yt.utilities.lib.element_mappings cimport ( Tet2Sampler3D, W1Sampler3D, ) +from yt.utilities.lib.coordinate_utilities cimport MixedCoordBBox from .vec3_ops cimport cross, dot, subtract @@ -2020,3 +2020,166 @@ def normalization_1d_utility(np.float64_t[:] num, for i in range(num.shape[0]): if den[i] != 0.0: num[i] = num[i] / den[i] + + +@cython.cdivision(True) +@cython.boundscheck(False) +@cython.wraparound(False) +def pixelize_off_axis_mixed_coords( + MixedCoordBBox bbox_handler, + np.float64_t[:,:] buff, + np.float64_t[:,:] buff_pos0, + np.float64_t[:,:] buff_pos1, + np.float64_t[:,:] buff_pos2, + np.float64_t[:] pos0, + np.float64_t[:] pos1, + np.float64_t[:] pos2, + np.float64_t[:] dpos0, + np.float64_t[:] dpos1, + np.float64_t[:] dpos2, + np.float64_t[:] plane_c, + np.float64_t[:] plane_normal, + np.float64_t[:] plane_east, + np.float64_t[:] plane_north, + np.int_t[:] indices, + np.float64_t[:] data, + bounds, + *, + int return_mask=0, + np.float64_t edge_tol=1e-12, +): + + # pixelize a cartesian image plane passing through a non-cartesian geometry. + # + # buff + # image buffer 2d array + # buff_pos0, buff_pos1, buff_pos2 + # native coordinates of image pixels, 2d arrays + # pos0, pos1, pos2 + # data coordinates in native coordinates + # dpos0, dpos1, dpos2 + # element widths in native coordinates + # plane_c + # the cartesian coordinates of the plane center point + # plane_normal + # normal vector for the plane + # plane_east + # in-plane +x vector + # plane_north + # in-plane +y vector + # indices + # index mapping for the data values + # data + # the data + # bounds + # bounds of the image plain in in-plane coordinates + # return_mask + # flag to return a mask + # edge_tol + # tolerance for checking whether a pixel falls within an element. defaults + # to 1e-12. + # + # most of this method is identical to pixelize_off_axis_cartesian. The initial + # identification of element-plane intersection uses the cartesian bounding + # boxes. When iterating over potential image pixels that intersect, + # it then checks that the spherical coordinates of the pixel coordinates + # fall within the spherical volume element. + # + + cdef np.float64_t x_min, x_max, y_min, y_max + cdef np.float64_t width, height, px_dx, px_dy, ipx_dx, ipx_dy, md + cdef int i, j, p, ip + cdef int x_ind_l, x_ind_r, y_ind_l, y_ind_r + cdef np.float64_t dxsp, dysp, dzsp, dsp + cdef np.float64_t pxsp, pysp + cdef np.ndarray[np.int64_t, ndim=2] mask + cdef np.float64_t xyz_i[3] + cdef np.float64_t dxyz_i[3] + + x_min = bounds[0] + x_max = bounds[1] + y_min = bounds[2] + y_max = bounds[3] + width = x_max - x_min + height = y_max - y_min + px_dx = width / ( buff.shape[0]) + px_dy = height / ( buff.shape[1]) + ipx_dx = 1.0 / px_dx + ipx_dy = 1.0 / px_dy + if pos0.shape[0] != data.shape[0] or \ + pos1.shape[0] != data.shape[0] or \ + pos2.shape[0] != data.shape[0] or \ + dpos0.shape[0] != data.shape[0] or \ + dpos2.shape[0] != data.shape[0] or \ + dpos2.shape[0] != data.shape[0] or \ + indices.shape[0] != data.shape[0] : + raise YTPixelizeError("Arrays are not of correct shape.") + mask = np.zeros((buff.shape[0], buff.shape[1]), "int64") + + with nogil: + for ip in range(indices.shape[0]): + p = indices[ip] + dsp = data[p] + # get the cartesian bounding box for this element + bbox_handler.get_cartesian_bbox(pos0[p], + pos1[p], + pos2[p], + dpos0[p], + dpos1[p], + dpos2[p], + xyz_i, + dxyz_i) + + # project cartesian bounds onto plane + pxsp = 0.0 + pysp = 0.0 + for idim in range(3): + xyz_i[idim] = xyz_i[idim] - plane_c[idim] + pxsp += xyz_i[idim] * plane_east[idim] + pysp += xyz_i[idim] * plane_north[idim] + #pzsp += xyz_i[i] * plane_normal[i] + + dxsp = dxyz_i[0] * 0.5 + dysp = dxyz_i[1] * 0.5 + dzsp = dxyz_i[2] * 0.5 + + # Any point we want to plot is at most this far from the center + md = 2.0 * math.sqrt(dxsp*dxsp + dysp*dysp + dzsp*dzsp) + if pxsp + md < x_min or \ + pxsp - md > x_max or \ + pysp + md < y_min or \ + pysp - md > y_max: + continue + + # identify pixels that fall within the cartesian bounding box + x_ind_l = fmax(((pxsp - md - x_min)*ipx_dx),0) + x_ind_r = fmin(((pxsp + md - x_min)*ipx_dx + 1), buff.shape[0]) + y_ind_l = fmax(((pysp - md - y_min)*ipx_dy),0) + y_ind_r = fmin(((pysp + md - y_min)*ipx_dy + 1), buff.shape[1]) + + for i in range(x_ind_l, x_ind_r): + for j in range(y_ind_l, y_ind_r): + # final check to ensure the actual spherical coords of the + # pixel falls within the spherical volume element. a small + # tolerance for the edge check accounts for floating point + # errors in the coordinate transformation (an edge_tol of + # 0.0 may result in blank pixels). + if buff_pos0[i,j] < pos0[p] - 0.5 * dpos0[p] - edge_tol or \ + buff_pos0[i,j] > pos0[p] + 0.5 * dpos0[p] + edge_tol or \ + buff_pos1[i,j] < pos1[p] - 0.5 * dpos1[p] - edge_tol or \ + buff_pos1[i,j] > pos1[p] + 0.5 * dpos1[p] + edge_tol or \ + buff_pos2[i,j] < pos2[p] - 0.5 * dpos2[p] - edge_tol or \ + buff_pos2[i,j] > pos2[p] + 0.5 * dpos2[p] + edge_tol: + continue + mask[i, j] += 1 + # make sure pixel value is not a NaN before incrementing it + if buff[i,j] != buff[i,j]: buff[i,j] = 0.0 + buff[i, j] += dsp + + for i in range(buff.shape[0]): + for j in range(buff.shape[1]): + if mask[i,j] == 0: continue + buff[i,j] /= mask[i,j] + + if return_mask: + return mask!=0 diff --git a/yt/utilities/lib/tests/test_coordinate_utilities.py b/yt/utilities/lib/tests/test_coordinate_utilities.py new file mode 100644 index 00000000000..ca8f6d62eaa --- /dev/null +++ b/yt/utilities/lib/tests/test_coordinate_utilities.py @@ -0,0 +1,148 @@ +import numpy as np + +from yt.utilities.lib.coordinate_utilities import ( + SphericalMixedCoordBBox, + cartesian_bboxes, + cartesian_to_spherical, + spherical_to_cartesian, +) + + +def test_cartesian_bboxes_for_spherical(): + + # this test checks the special cases where + # a spherical volume element crosses an axis + # or when an element edge is lined up with an axis + + # check element that includes theta=0 as an edge + r = np.array([0.95]) + dr = np.array([0.1]) + theta = np.array([0.05]) + dtheta = np.array([0.1]) + phi = np.array([0.05]) + dphi = np.array([0.05]) + + x = np.full(r.shape, np.nan, dtype="float64") + y = np.full(r.shape, np.nan, dtype="float64") + z = np.full(r.shape, np.nan, dtype="float64") + dx = np.full(r.shape, np.nan, dtype="float64") + dy = np.full(r.shape, np.nan, dtype="float64") + dz = np.full(r.shape, np.nan, dtype="float64") + + bbox_handler = SphericalMixedCoordBBox() + + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + assert z + dz / 2 == 1.0 + assert np.allclose(x - dx / 2, 0.0) + assert np.allclose(y - dy / 2, 0.0) + + # now theta = np.pi + theta = np.array([np.pi - dtheta[0] / 2]) + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + assert z - dz / 2 == -1.0 + assert np.allclose(x - dx / 2, 0.0) + assert np.allclose(y - dy / 2, 0.0) + + # element at equator, overlapping the +y axis + theta = np.array([np.pi / 2]) + phi = np.array([np.pi / 2]) + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + + assert y + dy / 2 == 1.0 + assert np.allclose(x, 0.0) + assert np.allclose(z, 0.0) + + # element at equator, overlapping the -x axis + phi = np.array([np.pi]) + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + + assert x - dx / 2 == -1.0 + assert np.allclose(y, 0.0) + assert np.allclose(z, 0.0) + + # element at equator, overlapping the -y axis + phi = np.array([3 * np.pi / 2]) + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + + assert y - dy / 2 == -1.0 + assert np.allclose(x, 0.0) + assert np.allclose(z, 0.0) + + # element at equator, overlapping +x axis + phi = dphi / 2.0 + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + assert x + dx / 2 == 1.0 + + # element with edge on +x axis in -theta direction + theta = np.pi / 2 - dtheta / 2 + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + assert x + dx / 2 == 1.0 + + # element with edge on +x axis in +theta direction + theta = np.pi / 2 + dtheta / 2 + cartesian_bboxes(bbox_handler, r, theta, phi, dr, dtheta, dphi, x, y, z, dx, dy, dz) + assert x + dx / 2 == 1.0 + + # finally, check that things work OK with a wide range of + # angles + + r_edges = np.linspace(0.4, 1.0, 10, dtype="float64") + theta_edges = np.linspace(0, np.pi, 10, dtype="float64") + phi_edges = np.linspace(0.0, 2 * np.pi, 10, dtype="float64") + + r = (r_edges[0:-1] + r_edges[1:]) / 2.0 + theta = (theta_edges[0:-1] + theta_edges[1:]) / 2.0 + phi = (phi_edges[0:-1] + phi_edges[1:]) / 2.0 + + dr = r_edges[1:] - r_edges[:-1] + dtheta = theta_edges[1:] - theta_edges[:-1] + dphi = phi_edges[1:] - phi_edges[:-1] + + r_th_ph = np.meshgrid(r, theta, phi) + d_r_th_ph = np.meshgrid(dr, dtheta, dphi) + r_th_ph = [r_th_ph[i].ravel() for i in range(3)] + d_r_th_ph = [d_r_th_ph[i].ravel() for i in range(3)] + + x_y_z = [np.full(r_th_ph[0].shape, np.nan, dtype="float64") for _ in range(3)] + d_x_y_z = [np.full(r_th_ph[0].shape, np.nan, dtype="float64") for _ in range(3)] + + cartesian_bboxes( + bbox_handler, + r_th_ph[0], + r_th_ph[1], + r_th_ph[2], + d_r_th_ph[0], + d_r_th_ph[1], + d_r_th_ph[2], + x_y_z[0], + x_y_z[1], + x_y_z[2], + d_x_y_z[0], + d_x_y_z[1], + d_x_y_z[2], + ) + + assert np.all(np.isfinite(x_y_z)) + assert np.all(np.isfinite(d_x_y_z)) + + # and check the extents again for completeness... + for i in range(3): + max_val = np.max(x_y_z[i] + d_x_y_z[i] / 2.0) + min_val = np.min(x_y_z[i] - d_x_y_z[i] / 2.0) + assert max_val == 1.0 + assert min_val == -1.0 + + +def test_spherical_cartesian_roundtrip(): + xyz = [np.linspace(0, 1, 10) for _ in range(3)] + xyz = np.meshgrid(*xyz) + xyz = [xyzi.ravel() for xyzi in xyz] + x, y, z = xyz + + r, theta, phi = cartesian_to_spherical(x, y, z) + x_out, y_out, z_out = spherical_to_cartesian(r, theta, phi) + + assert np.allclose(x_out, x) + assert np.allclose(y_out, y) + assert np.allclose(z_out, z) + assert np.max(r) == np.sqrt(3.0)